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e0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cn04AAEFNAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAIAAAAAAAAAAAEAAAAAAAAAJxJ%2FLAAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAA5ADsAUABTACAAMAAwADEALgAwADAAOQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAKncAE0TAE0ZAEbuFM3pjM%2FSEdmjKHUbyow8ATBE40IvWA3vTu8LiABDQ%2BpexwUMcm1SMnNryctQSiI1K5ZnbOlXKmnVV5YvRe6RnNMFNCOs1KNVpn6yZhCJkRtVRNzEufeIq7HgSrcx4S8h%2Fv4vnrrKc6oCNxmSk2uKlZQHBii6iKFoH0746ThvkO1kJHlxjrkxs%2BLWORaDQBEtiYJIR5IB9Bi1UyL4Rmr0BNigNkMzlKQmnofBHviqVzUxwdMb3NdCn69hy%2BpRYVKGVS%2F1tnsqv4LL7wCCPZZAZPT4aCShHjHJVNuXbmMrY5LeQaGnvAkXlVrJgKRAUdFjrWEah9XebPeQMj7KS7DIBAFt8ycgC5PLGUOHSE3ErGZCiViNLL5ZARfywnCoZaKQCu6NuFX42AEeKtKUGnr%2FCm2Cy8tpFhBPMW5Fxi4Qm4TkDWh4IWFDClhU2hRWosUWqcKLlgyXB%2BlSHaWaHiWlBAR8SeSgSPCQxdVQgzUixWKSTrIQEbU94viDctkvX%2BVSjJuUmV8L4CXShI11esnp0pjWNZIyxKHS4wVQ2ime1P4RnhvGw0aDN1OLAXGERsB7buFpFGGBAre4QEQR0HOIO5oYH305G%2BKspT%2FFupEGGafCCwxSe6ZUa%2B073rXHnNdVXE6eWvibUS27XtRzkH838mYLMBmYysZTM0EM3A1fbpCBYFccN1B%2FEnCYu%2FTgCGmr7bMh8GfYL%2BBfcLvB0gRagC09w9elfldaIy%2FhNCBLRgBgtCC7jAF63wLSMAfbfAlEggYU0bUA7ACCJmTDpEmJtI78w4%2FBO7dN7JR7J7ZvbYaUbaILSQsRBiF3HGk5fEg6p9unwLvn98r%2BvnsV%2B372uf1xBLq4qU%2F45fTuqaAP%2BpssmCCCTF0mhEow8ZXZOS8D7Q85JsxZ%2BAzok7B7O%2Ff6J8AzYBySZQB%2FQHYUSA%2BEeQhEWiS6AIQzgcsDiER4MjgMBAWDV4AgQ3g1eBgIdweCQmCjJEMkJ%2BPKRWyFHHmg1Wi%2F6xzUgA0LREoKJChwnQa9B%2B5RQZRB3IlBlkAnxyQNaANwHMowzlYSMCBgnbpzvqpl0iTJNCQidDI9ZrSYNIRBhHtUa5YHMHxyGEik9hDE0AKj72AbTCaxtHPUaKZdAZSnQTyjGqGLsmBStCejApUhg4uBMU6mATujEl%2BKdDPbI6Ag4vLr%2BhjY6lbjBeoLKnZl0UZgRX8gTySOeynZVz1wOq7e1hFGYIq%2BMhrGxDLak0PrwYzSXtcuyhXEhwOYofiW%2BEcI%2Fjw8P6IY6ed%2BetAbuqKp5QIapT77LnAe505lMuqL79a0ut4rWexzFttsOsLDy7zvtQzcq3U1qabe7tB0wHWVXji%2BzDbo8x8HyIRUbXnwUcklFv51fvTymiV%2BMXLSmGH9d9%2BaXpD5X6lao41anWGig7IwIdnoBY2ht%2FpO9mClLo4NdXHAsefqWUKlXJkbqPOFhMoR4aiA1BXqhRNbB2Xwi%2B7u%2FjpAoOpKJ0UX24EsrzMfHXViakCNcKjBxuQX8BO0ZqjJ3xXzf%2B61t2VXOSgJ8xu65QKgtN6FibPmPYsXbJRHHqbgATcSZxBqGiDiU4NNNsYBsKD0MIP%2FOfKnlk%2FLkaid%2FO2NbKeuQrwOB2Gq3YHyr6ALgzym5wIBnsdC1ZkoBFZSQXChZvlesPqvK2c5oHHT3Q65jYpNxnQcGF0EHbvYqoFw60WNlXIHQF2HQB7zD6lWjZ9rVqUKBXUT6hrkZOle0RFYII0V5ZYGl1JAP0Ud1fZZMvSomBzJ710j4Me8mjQDwEre5Uv2wQfk1ifDwb5ksuJQQ3xt423lbuQjvoIQByQrNDh1JxGFkOdlJvu%2FgFtuW0wR4cgd%2BZKesSV7QkNE2kw6AV4hoIuC02LGmTomyf8PiO6CZzOTLTPQ%2BHW06H%2Btx%2BbQ8LmDYg1pTFrp2oJXgkZTyeRJZM0C8aE2LpFrNVDuhARsN543%2FFV6klQ6Tv1OoZGXLv0igKrl%2FCmJxRmX7JJbJ998VSIPQRyDBICzl4JJlYHbdql30NvYcOuZ7a10uWRrgoieOdgIm4rlq6vNOQBuqESLbXG5lzdJGHw2m0sDYmODXbYGTfSTGRKpssTO95fothJCjUGQgEL4yKoGAF%2F0SrpUDNn8CBgBcSDQByAeNkCXp4S4Ro2Xh4OeaGRgR66PVOsU8bc6TR5%2FxTcn4IVMLOkXSWiXxkZQCbvKfmoAvQaKjO3EDKwkwqHChCDEM5loQRPd5ACBki1TjF772oaQhQbQ5C0lcWXPFOzrfsDGUXGrpxasbG4iab6eByaQkQfm0VFlP0ZsDkvvqCL6QXMUwCjdMx1ZOyKhTJ7a1GWAdOUcJ8RSejxNVyGs31OKMyRyBVoZFjqIkmKlLQ5eHMeEL4MkUf23cQ%2F1SgRCJ1dk4UdBT7OoyuNgLs0oCd8RnrEIb6QdMxT2QjD4zMrJkfgx5aDMcA4orsTtKCqWb%2FVeyceqa5OGSmB28YwH4rFbkQaLoUN8OQQYnD3w2eXpI4ScQfbCUZiJ4yMOIKLyyTc7BQ4uXUw6Ee6%2FxM%2B4Y67ngNBknxIPwuppgIhFcwJyr6EIj%2BLzNj%2FmfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h%2FC%2FPkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn%2FpdpgHKNGrGIdkRK%2BKPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho%2BEIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA%2Bq%2Fj9m3LM%2FO7WJka4tSidVCjsvo2lQ%2F2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3%2F5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho%2FbcwQdeboqfQartuU3CsCf%2BcXkgYAqp%2F0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid%2FNIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A%2BTRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC%2BJE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW%2FWDHA60cYFaI%2FPjpzquUqdaYGcIq%2BmLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC%2B1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A%2BP%2FoFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E%2FvwOiKxRtCWsDM%2BeTHUrmwrCK5BIfMzGkD%2B0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3%2FkFutpQGNc3pCR7gvC4sgwbupDu3DyEN%2BW6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc%2Bh1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0%2BbC5zgpGz7Io%2BmjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO%2FENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn%2B3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif%2BpZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx%2Fk3QgnAFSq27%2F2i4GEBA%2BUvTJKK%2F9eISNvG46Em5RZfjTYLdeD8kdXHyrwId%2FDQZUaMCY4gGbke2C8vfjgV%2FY9kkRQOJIn%2FxM9INZSpiBnqX0Q9GlQPpPKAyO5y%2BW5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W%2FN6l54qOynCqD3DpWQ%2BmpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE%2B7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv%2F8lbTIkkYpqKM9VOhp65ktYk%2BQ46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI%2BejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5%2B7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Ffont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFuAAA8AAAAAsVwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABWAAAABwAAAAcbSqX3EdERUYAAAF0AAAAHwAAACABRAAET1MvMgAAAZQAAABFAAAAYGe5a4ljbWFwAAAB3AAAAsAAAAZy2q3jgWN2dCAAAAScAAAABAAAAAQAKAL4Z2FzcAAABKAAAAAIAAAACP%2F%2FAANnbHlmAAAEqAAATRcAAJSkfV3Cb2hlYWQAAFHAAAAANAAAADYFTS%2FYaGhlYQAAUfQAAAAcAAAAJApEBBFobXR4AABSEAAAAU8AAAN00scgYGxvY2EAAFNgAAACJwAAAjBv%2B5XObWF4cAAAVYgAAAAgAAAAIAFqANhuYW1lAABVqAAAAZ4AAAOisyygm3Bvc3QAAFdIAAAELQAACtG6o%2BU1d2ViZgAAW3gAAAAGAAAABsMYVFAAAAABAAAAAMw9os8AAAAA0HaBdQAAAADQdnOXeNpjYGRgYOADYgkGEGBiYGRgZBQDkixgHgMABUgASgB42mNgZulmnMDAysDCzMN0gYGBIQpCMy5hMGLaAeQDpRCACYkd6h3ux%2BDAoPD%2FP%2FOB%2FwJAdSIM1UBhRiQlCgyMADGWCwwAAAB42u2UP2hTQRzHf5ekaVPExv6JjW3fvTQ0sa3QLA5xylBLgyBx0gzSWEUaXbIoBBQyCQGHLqXUqYNdtIIgIg5FHJxEtwqtpbnfaV1E1KFaSvX5vVwGEbW6OPngk8%2FvvXfv7pt3v4SImojIDw6BViKxRgIVBaZwVdSv%2BxvXA%2BIuzqcog2cOkkvDNE8Lbqs74k64i%2B5Sf3u8Z2AnIRLbyVCyTflVSEXVoEqrrMqrgiqqsqqqWQ5xlAc5zWOc5TwXucxVnuE5HdQhHdFRHdNJndZZndeFLc%2FzsKJLQ%2FWV6BcrCdWkwspVKZVROaw0qUqqoqZZcJhdTnGGxznHBS5xhad5VhNWCuturBTXKZ3RObuS98pb9c57k6ql9rp2v1as5deb1r6s9q1GV2IrHSt73T631424YXzjgPwqt%2BRn%2BVG%2BlRvyirwsS%2FKCPCfPytPypDwhj8mjctRZd9acF86y89x55jxxHjkPnXstXfbt%2FpNjj%2FnwXW%2BcHa6%2FSYvZ7yEwbDYazDcIgoUGzY3h2HtqgUcs1AFPWKgTXrRQF7xkoQhRf7uF9hPFeyzUTTSwY6EoUUJY6AC8bSGMS4Ys1Au3WaiPSGGsMtkdGH2rzJgYHAaYjxIwQqtB1CnYkEZ9BM6ALOpROAfyqI%2FDBQudgidBETXuqRIooz4DV0AV9UV4GsyivkTEyMMmw1UYGdhkuAYjA5sMGMvIwCbDDRgZeAz1TXgcmDy3YeRhk%2BcOjCxsMjyAkYFNhscwMrDJ8BQ2886gXoaRhedQvyTSkDZ7uA6HLLQBI5vGntAbGHugTc53cMxC7%2BE4SKL%2BACOzNpk3YWTWJid%2BiRo5NXIKM3fBItAPW55FdJLY3FeHBDr90606JCIU9Jk%2BMs3%2FY%2F8L8jUq3y79bJ%2F0%2F%2BROoP4v9v%2F4%2Fmj%2Bi7HBXUd0%2FelU6IHfHt8Aj9EPGAAoAvgAAAAB%2F%2F8AAnjaxb0JfBvVtTA%2BdxaN1hltI1m2ZVuSJVneLVlSHCdy9oTEWchqtrBEJRAgCYEsQNhC2EsbWmpI2dqkQBoSYgKlpaQthVL0yusrpW77aEubfq%2Fly%2BujvJampSTW5Dvnzmi1E%2Bjr%2F%2F3%2BXmbu3Llz77nnbuece865DMu0MAy5jGtiOEZkOp8lTNeUwyLP%2FDH%2BrEH41ZTDHAtB5lkOowWMPiwayNiUwwTjE46AI5xwhFrINPXYn%2F7ENY0dbWHfZAiTZbL8ID%2FInAd5xz2NpIH4STpDGonHIJNE3OP1KG4ISaSNeBuITAyRLgIxoiEUhFAnmUpEiXSRSGqAQEw0kuyFUIb0k2gnGSApyBFi0il2SI5YLGb5MdFjXCey4mNHzQ7WwLGEdZiPPgYR64we8THZHAt%2BwnT84D%2Fx8YTpGPgheKH4CMEDVF9xBOIeP3EbQgGH29BGgpGkIxCMTCW9qUTA0Zsir%2BQUP1mt%2BP2KusevwIO6Bx%2FIaj8%2FOD5O0VNrZW2EsqZBWbO1skRiEKE0DdlKKaSVO5VAuRpqk8VQJAqY7ydxaK44YJvrO2EWjOoDBoFYzQbDNkON%2BUbiKoRkywMWWf1j4bEY2iIY1AeMgvmEz%2FkVo9v4FSc%2FaMZMrFbjl4zWLL0%2BY5FlyzNlEVYDudJohg8gPUP7kcB%2Fmn%2BG6cd%2B5PV4Q72dXCgocWJADBgUuDTwiXiGSyZo14HOEQ2lE6k0XDIEusexDzZOMXwt1Dutz%2BtqmxTvlskNWXXUQIbhaurum9GrePqm9Yaeabjkiqf%2BbUvzDOvb2Y1E%2BEX2DnemcTP%2FzLcuu7xjQXdAtjR0Lo5n4%2FHs%2FGtntMlysHt%2B29NXbH6se%2F%2FWbFcyu%2Br28H0MwzI30DYeYTLMXIA2EG8QlHpAsyS0EfEToR0a3utIxFPJ3kiIHCCrZ66b0e2xEmL1dM9YN%2FMwS5p01N5jMX%2FBLKt%2F1R83l0LyC29M6%2BiYxo%2FUNg%2FEF7c2WyyW5tYl8WnhWg2%2FhyySbD5UhnDyS7OcU0dnrFw%2BDfGdI7v4QfYIIzOMq9hFtY55gmvC7jZ2FK7sEdrn6IXBuucYhjsGdQ8z0yEbWkkczjjsE5hNAIZrPx2zOLZDmKNXcXtg7EMqidAEEWg%2BSJCBBNwxvxJfc%2FbZa%2BKKf%2BxoKZybnq5vaqpPTye7CiF%2BZFjxZ8%2F7Qij0hfOG%2FcowPA1rT1l4ymWnrKmxxqfErTVrpgwPlz1kC%2BOy8NMDz6c%2BIO38K%2Fx0xkPnLW8Kx6qGAoQdL%2BTD9V9rb%2B%2Fctn%2F%2Ftrxz8dUrZrD%2Fzk%2FferF0cNt1BzctmX2FZPXt%2FjnFCQNz4Ah%2FiKllGiCMs1w5Lkg0kiEwj6VTXCDKsX9rMpnvIj9pcDecXAIXMnqn2dTUbN6w0XQ9ue6FV%2FnnXCH7S3lPWGltVcLsH75ub3ab7A8M28caNrIeOr3o5Q0yFsYL80xaa0EY%2FUEczV7icUMY5pnelAkmUAXmHYjvFWFGxuqlSaow3OM%2B%2FiYY7%2Fl%2FhVELF4EjRqNR%2FbvRbOY%2BDUGzGR%2FOh3EqmE%2FugIQQguGt%2FeMYz%2F%2BL0cimjeZfQDI3phXMbMQsqH%2BCjwVz%2Fhf4idHovgVmB8gLvjbicDcC%2FNypP536E%2F9N%2FpuMibExdohBmNwyiaZdJGoigos7GpF222xrfnZhML%2F7Z%2BylaqP63Hr%2Bm7bdUkQ6%2F2cXqdfmvwixY%2Bs2ksXFeXcE%2BiX0Z%2BIow76DBNgjJ7TOdUK18iPsPflfQD%2BDPsZG2Aj9VmKMMJ4fYRrhIaxhTDR0Elh2vA6h%2FAE6xUb29mj3sjmL72petXjejPy%2Boel60M99tFduCI59N3221xe7apOvxs6aHs7vab1IqY2tv7q2xsHeHGml%2FcV06u%2F8S%2FxTjJ%2BJYc0bWEX0ukW6YmIbGkJRMdjJ9mYIH5QIdJF4hvRGyK7cC7ctImQRcUET99fGXOoft35GYLMQu%2Bg2smnkgZUrH8AL%2F9Si217IssJ916nv14ZrJrvdxLkQvrvtBcjgPC0NXOicO8Qf4mcxPqh3hgUw3DDfdvLJXngg7N3dN2zbPJSaed3OfZnMU7dvmznp3C3bruO%2BNmue0LFsy7S%2B6265%2BfCKFYdvvuW6vmlblnUI8xCXp37CrOZv4B9gauDBlYp7adcUXB5DNCwYImlXOJJKkAdvExXxVvKEYnCo%2B3eIskP9qrrfIYs71CccBjfXRC52udTHHdaP1A1ui%2FVvH1otbrLrpNXBsGX5B89QghDyimlvNB2KfkxZ5C9%2Fem3%2Bd1%2Bd%2F%2FIfFp2%2B2Oxn%2Fs%2B9n%2F79p39S3s8idN6g0yZObwJOgKUpNB3GyU0Ls0PbRzIRq4lcarLKOJBkLRzJQD4j2090XrbA7DW8K3jNF5hlGS5e4V2D17zgss4T20egOJte5iD0bReM9yjTxnQxCRj3c5kFzGJmGbNKmwGw39IJDJcXJZGMkaAB4jyJAKw0jt5IAuIE%2BA%2BU3cVAZZrq9zhDyBrU8oosuxcGNTzCKJfla7JjNVmuSb%2F%2BtuzN2H%2BX4vlB%2BPpdfMXXmuVsNiub1T34SFbjYw5itEvVi0K0Nt9pNJUMI7SLGRhf2xipfCYf8z5OdlGKayOucFeVPeS%2Fdbo3lBrbSMmwUiQN5%2Fed7g0Ds1s17IuZC5kNzM3MZ6EWCa0DtekdJfAxz%2BR%2FOX28sND7yRMTBcf%2B%2Bs8mQCQWHya4qBv%2FufeMoWyslPA9DtMxUknxkH%2FyfTnm2CMYzs%2BCq3r7PxY%2FMXomrvTEsRpfEGHa%2BWN8E1AHjElb7d06ddA7oK%2F%2B5Mdsv9EtPms0jv0Z5kf1FqPxWdFtfFr0kHfgDX0Y%2B5PRSG7RUj0tQr7rmfX8DH4G5W28kKeJLtmQsQkuwMP1pk16EV4sl7vrMJATfyUWo%2FGwEco4rh4XFQgaiUX9qxZHrMQqKnz%2Fc2d8b9TysYrAuXpP%2FRf%2FGr8b1qwwc5a%2BeuLa6S6sneNXToG2XrEJi4R5SGs8Sq2S3d97bsfCRaTdaLwKClRHt37mkudvXbjwVrLhuYeGhh56bvfQkHpk2CwvwClqgWwuBfndC3c8dwmstj81KkagcUgbfPY8Zje0W%2F82VPWJHmSq6pP8hPWpotc%2FEexDOK3qU%2BwngPhOCiO9MJRm8TJefjelrzoKnG2Bn%2B1NCUmPE4gHFmBN9jrTigRIpsACrc9Gstg58ULkp9467%2BGf%2FeFnD5%2F31lNrt2967dhrm7bzI%2BVT5m%2BfzKhvf2MzpICEm79Bopkn07lt1762adNr127LwVqQLdJ5%2BlpQDcvHPQtVY5knhYrK6q8%2FJsiP6EuhGZdFdaNszjvpqvc%2BPI0CdjN0AXsFOC3ZfALDJwr4q2Xq%2BGF%2BGNbsxUg5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg%2BFB1ubjlnRNvl3o6IEU2w7fdNPhm%2Fhh%2BFLysUu6%2B%2BDLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK%2Fm4szFFR7ch0toUgBTdWHr7EpaWru6%2B6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d%2FjLST9v5YflasP6v%2FCO7%2BGNAPC2BMZWmsOjp2NNbfHwMCJD%2BLPVL%2BD%2FOYlWEEI%2F9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2%2BbBDWBud1Va4pcCn8CPqxlh%2FfgtG8IPaPH8C5wk6%2FnZDv69jurV5QhtwE0x2iqOsj9Mx8B9%2F0EaUdiPfOYYDCi%2Fq9jhWRuupMDEU0%2BCtX0sDFxv07T%2FK5niBPqN9%2BtQjgEc31NGCXFeMcCEuQBIc%2FBK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF%2FR0%2Fu5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y%2BRBgogrXPZ8E1yIHoHIFUM%2BAbJhE7lbMtt8ApL%2BxmZW7PwbjAO0fAVoXQOuiSP%2FksIVdFZ0aulsamKUzwPZ%2FNYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7%2Bpg%2BjUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd%2FJ5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7%2BRHJHP%2B2hqfugo%2FMvI2H%2Fmqr4b9tFnKSRY1Y5Ek80Nm%2FWIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw%2BnSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv%2BrWa%2FWih0vlbX6Zb75T5C0qNKVFvH1QL%2FvazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f%2BXdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS%2FxTo%2BP5wGFak62ap1PVFFN4v%2Fy%2BxuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f%2F6dhK6gQokFKhWX3urrjk%2FrnI0pgfpGMeuQIUaEM7%2BGF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x%2FGdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY%2B9yGFQtEo0GQPNv6vS2drj4%2B1jHbv3aJSMUWP%2BQTZrmbNTjU8wyG%2FiXNNpskybLcJ3CiTF5Ir%2BJYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d%2B%2FXeNRTZ9%2F1OPfG7%2B2hwzd5W3D%2BhmyjsRcUg%2F%2BCavb%2B%2BVh2ls3L7zT%2FetOnHNxeerv313vzLVqPai4nJv%2BK1FC6040%2F4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk%2FU%2BD4gTXW%2Bj0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7%2FR561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l%2BWKHmLTJwRv9E8GWJ6dYvf%2FFmEyEGr%2Bgyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b%2BtMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1%2BQ2BIRvf6J6Kgatnrbiem%2BCFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ%2BCuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih%2FxELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD%2BuOe%2BkWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby%2F%2BSSbPY8%2FNH6vpl%2FEsq3Ae4ZU1HC44KFiI9o7CEgab%2FRqHbj7s5KAg06s39ZP%2FzxI%2FmVuF%2FTbTSy%2B3Fb8If9%2Fcv7%2Bwt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi%2FC8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0%2BXxx%2F5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB%2FqM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J%2FUKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc%2FTHK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd%2B%2BU%2F3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg%2Be4As38MfGquNt7oUws6Ysarj1%2FefE%2Byst86YUVNvDdts3Pv5c8m%2FaP0C%2Bf8%2FQb%2BIMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT%2Fv27wm09jRYVc%2Fe%2B%2Biyx2tyzJb%2Fn3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy%2Ft27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj%2FS5ki1jL%2Fa0GC6%2B2L6Um%2BaoddlNFuj%2BbJ8mH%2FiaLh8I0%2FU51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz%2B1UmhTJyJGxZzw%2BwsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ%2F9pPA4RKTASWahmh%2B8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ%2FtHnODtENw%2F2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8%2B%2FElg5j4Gxur3J8o2PJ4rg%2B2d08T%2FfwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r%2FaMMj8onCP%2FDuDZOuN%2BGPPr%2F%2Bp7bx%2B7JlbYdppcNhzKU%2F1Px5aiaGDn%2Fs1iGMaBcleKUo%2Fv9rcxkZj7DBEKOfrayytXNLYiUdBY%2BpleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF%2FQa9Pdf%2FQqOSqCiE%2FEE1%2FXIVKTc2tzWbHnimrEd%2BVyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu%2FkzIdx%2BBxC0ay9zRSvoS0F2lIxI%2BX7klU63I40gLQ3w5ep5na%2BSFnba3z5D64zv%2BQtM4n4ffG3tq4aNHGRfxgrXPMim%2B5487abL7xhdseIRn1KDl%2B7aINixdv0OD%2BJSPwKf5%2BxoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz%2B9a%2Fv2PH1Hfz9236w%2BZrPXvWfAxlj4NLLHpq3c%2FPQ3uvmvbrjG7fe%2Bo2y%2FcLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF%2FtkkOGPW2ibWaBTkeZ%2FdvPWazXfOnnvL6jkRXpi85sFzZt%2B55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG%2FyVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6%2FOU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR%2Frg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x%2BMJiOA3YwhDRQrWU0u%2F0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0%2Ba0uoiFH%2FxcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL%2Bj3D%2FuwhBRdyAyozeZwvQzs79soi%2BBKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo%2FybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM%2B3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho%2FVDkzQfLE%2BIrYoJXkD19pdP7OwG%2FvoIUtagiWiZ4PAFTHHlTVhRZ7dYmPar%2BNJ%2B8JhmR6DFK5DV1foHoLNO%2FpHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj%2Bi5%2B%2BWZfKeViJfW5HnUakVL4UCNVkA4%2BETfIqx4B5xSaP2L1yn0zn2ltPn4%2BOqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL%2FNNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE%2BcPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP%2BKps8J9%2F3mGQIteY%2FLGPC%2BL7872SPR2br5fy8MtKBMHedGuM28%2FMZmPJMrGgi3Gb1S%2BSi1%2FL%2FzrZwO9XH1ce%2Fz7ZQ1WSoY%2F%2BpMb5FT4ua0Wm%2BJf%2F298nFmChEQ%2BTi71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa%2F1IFO1%2FjdWr%2F8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc%2BKJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3%2FtM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB%2Fyox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt%2FqMDnvLpPNTXfNa60z5%2FyjXQOMq%2BlNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd%2BRiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb%2FpBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap%2F30lnVESgNnvjbUoT6w9N%2BXoio0qcYOIM%2Bheg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X%2BPE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa%2BdAdAbMYX6HVF8A%2B7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf%2FNf%2BNy65BhYxxxV%2B77XJ2wfQ389%2FIQPgajXbwMsuAz%2F0IaQcXJavKbRqR2IqyZruXjVC2%2Bhdee%2F5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab%2FwXl2CNe7s%2FqCtTvWgG5kpBmCBlSzDS%2Fr8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8%2FNCfiOUVirYcBbIeA2%2BiF68rQIo3B%2FS628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935%2Bk1efkV51mzzrM0LL3%2F20avnwMeKuWyOUZg2TasSqZ%2BKcZQiOn1Iu2Vh497ALUVZiCKt%2Fgh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1%2FuzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb%2BokJ5JRDyf54Axur1D%2BWS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt%2Bt%2FqW2%2B93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz%2BtJo%2BwijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF%2BkuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj%2BZ4i%2B42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA%2FhRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz%2Bx%2BFkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3%2FRTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY%2BuSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN%2BfiK86LeXj%2BSc5lPKy%2Bk%2FvCUI%2FDaLFYCWHr6nbXuILTIsb5imNKY%2FrCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T%2FyOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH%2BL0eCPxmsa5ZpgRJSDZ11yDv%2Bjmbd86vxZfc1WcZJ3UkMq1BOOOVtvu%2F%2BpB%2Ben186d3GTwWAw2jheaJs09%2F%2BLNfZft37DALyrNj1wABMuUKbODyTVnT%2FKYbJ3Tpq8IrNh92dkxOj5P%2FYpZx4%2FycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB%2BcV99%2FeTF6294dA8%2BTK6v74MHVpYNRt%2FI30e8QGTOOdfGWzzxcy%2B87a7bLjw37rHw1nPzp0KyyRSeZO%2BQQhInt3dYgvycjrPOv%2BT8s1rptaP84VeywdWX2T4ysr0%2F7TLIs6%2Bx9zib56ye1dM9e%2FXsZmePY3NDs9zlnNVt4%2BWgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS%2BXPplV2ts4bvCwZu%2BKzgITtxepaPRzWdpv74muvv6RO0SorX6cu%2FdqKn%2FXWnrtp%2FZragz13DUCl5myiFW2Ycvb0PtsXnU%2Btx8pvLFbUspLX68mdegwmOif%2FNPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3%2FjdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx%2B6arPqbkcNAHoFPzKUUQ%2BqL0k97pjbZv1I%2FegC9zTFbrrlFpNdmea%2BgIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV%2FScS%2BzjlASyUTVv%2FAJ46gkJI4bHX4lTnloctxPZE1ckS3%2BjG2fKIjkQFyzuo8jvYQG1OrGvJPSTu%2FnSp9PHNTl4z5hK%2F8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt%2FwkSx%2FJCmP5%2FcBKQfl%2F5gd%2BrOS%2F%2Bp91%2F%2BYCg5CXK2W4M9fu%2B%2F6xxX%2BvnelVuldIDCG0VQTpU9Dw4pRfei%2B6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15%2B5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl%2F5bqzYqT9hMmptEXDgTqP3Wiye%2BsD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC%2Fv8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4%2BTi7W9D%2FOZrLChdwxmPNiBRqVjnpK%2FaGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3%2BOfU5%2FieLS%2FNuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD%2BN2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad%2F3t2KTtPobnX6D8C8pd0MDP%2BKx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE%2BJGTS3JoEhTrz8%2FCB%2B5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk%2BhtwN5A0V3RCPoD%2FyXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps%2Ft%2B0iCT3AHVtZC7JDCXfR7OSb%2FXja5H3zQbZL1B%2BULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset%2BnQWdS%2FZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5%2BLhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW%2F3Q5Nn8BZEuzqEI7HK3n0%2BzFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj%2FmQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2%2FBWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY%2FyCZu%2FAmd5Or9uS3DYaeqVOhH7gZN%2F8I%2Fwi1fEuLXvyNivibjuKvN%2B1Nc01HF%2F3h%2Bef%2FsOhox8MPd5SFucPjorQwXT%2BytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP%2F79nbvCXFOY%2BPUjrT7%2FotsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9%2Bpbs6E%2FVu7Nk642%2FPYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu%2F19%2F5QW9Rv1U1ridT22i%2B53pzumbs%2BXFFXYC%2B%2BCGsTj5JUT%2FGCgRt3n78i2n71FHG4%2Fu6X%2B%2B9%2Braya7os3ZbDmgWfXun44e%2Bu2NZKuGZ0HiF8M4TlMPR%2BEU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l%2BuXrbjPUyep%2B8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq%2BNzK%2FOKSbsfl79o9G20R%2BbrBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery%2F7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1%2BSdazRrfddkBU98t1htvWrbjqSqjaCguxrffM%2F5zDCpBALUycmajhd%2BR6ww4SWafuZ5eU%2BtPid4lgd3gt%2Bb%2FY9rQoZNmiXYPXyRHbRs8zX%2Ff4WIFjWZJtUdSD55AP3xtXH%2BZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy%2F8F0GoqYPv75Yh9j3x4DuJ%2BuEzHRpAq2lMqb%2BqfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT%2BLUgx0Pg%2Fp87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv%2Fu9ZUWUx%2BPYFueUKwaNvbtu%2BXps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L%2FBehXuHqfaBwBEU7hfVLcXvS4VQv%2BT%2FvaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq%2Bk4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S%2FtkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP%2BEaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP%2FdsZ8%2BfP75D%2F9Uu5Gs3FY%2F2SxPld0DHOciXI9gqjcEidXjE%2B3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8%2BLTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7%2B%2BwW4gRDRbaxw2nrOGm%2BxOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot%2FFpHvIUjJovFEoYvHYV9C5Y%2FxN9OfcalvII37UEhTbTg%2FAQIaPb4Vz6j5u8%2FaViycMod%2FfkDcpu8QZbZoeBi%2FvbzP3XPsZvOubMtaPHkD9jt6%2BU2O7vqU%2F9C9SMvgrXpQNG%2FE0oJxun%2BCiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp%2FVAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y%2BpGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr%2BP2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr%2BP30f3OkPQ72aO0xYo3%2FEsmO3QO5qEF8S0qQH0UsKXv0brnl9%2B8M7jF174%2BDsfvPOl1au%2FRL5%2F9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x%2B4ofw%2B3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ%2BpYl%2BSf8YcurfmXP5F%2Fkj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0%2FOM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw%2B1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g%2BRn533qkocdvLs2HmhU75br%2FMmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3%2F3pYNi3bS%2BRd%2BGjOfVpAPNd6y64Gsz1GaZleWIPoYL%2Fv9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk%2FIojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB%2F36kP%2BK38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL%2Bx03nhh%2BoNo5e77psxg9Q5LzebIKD%2BfsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O%2Fkdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P%2FF7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF%2FBRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm%2BRdmIJQST%2FZfVdudUvh9S%2FqqNvqT98g9SQ3lHibZY0mRVHooyDN%2FFHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi%2Bd0s4%2Bztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr%2B1VZFcEZ%2FPdJkn1hOs8SXS%2FNFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq%2Brbyg1Z%2BO8VsNC1UmcvORPRfxtPrfRwL2p%2FoA1eZp6Z%2FaGffoewaXcA%2FxBlKlQLfhQL%2FoPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp%2FbXh5e5YXW2Ngfvza0ZF6UgFL%2FE0fTq4LBlvTE2qb%2FKuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8%2FVWi33c3bBZnGY05%2Bdm%2B3qc7fNmj4YGKLj2nfqFP%2Bg7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j%2BSYEWHlZ0K%2FHgsh%2FZtsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D%2BYVyKd0jw3iumwi%2FBC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc%2FJ5uGm6CViW5mulYMk%2BHqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br%2BfcLyoAyu8D9B7bgmzUqfFobF5nKnK4%2Bt8MPJkI%2FxHUNWk117jugWF%2BxazTAALQn6%2BUE9lhoI5ApGA%2FiuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL%2F3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n%2BjspIsHFjJrTOdzSMuOa9DbDcj%2FnH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U%2F8JbJZPJJLBLolH1La%2FRbF9AbC8JJjv%2FmMnssKjLRBJyqj9QXxNko0Ux%2FX79epfiXkm6fmKwF%2Fen1HLc6LxloXWKvGa5rVCVL83VuiPcDEX%2FK5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS%2FkTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv%2F36k2sbqCeCFNa%2BURpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR%2BD79H56NOz0EVWCTy2%2FfffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ%2BT%2FEl3uZqL%2BFyzSZ8XxpTiI%2FG0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ%2BeuOeG2SORmKZr%2FqKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya%2BvlKqv6aXK1qIj3imL%2BL6hL%2ByvUlFfE0VKZ7E8gBY3M%2F8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry%2FLgZIlCeP11Z4zs%2FAwvVwtGFEut5S1JY4lfyT0N%2FevOLo%2BrUEgjcqc9IkGpQbv3iW7Co5b%2BKgjvpzYdH85PLcc4X21ouwEGl%2FS4qnUAvoSlXUUhR1eKr2VWFTB%2BGMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH%2FqPepXQ0Db77CJOAImohB%2BRPWr31ev5g%2FkE%2BzTa4lbvZo8xdWPffQu9yJTPCNB66s%2BzXoJt%2F0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0%2B3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80%2BUxfUHPlBZIRVNQ%2Bv0xRm8REKPoLmNr0%2BUo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV%2F%2Bmfr0C0r%2FNkeXbbpPlOTBBwT0mVz1zx9S%2FwJecBF9Wgv3p032iP2v4VSgfgW2G%2BHUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt%2FQRasn2kri%2BH6L80MJRsmVYJrAKyDItpJUy3%2F15WYIJqcJ9Q5N%2FLFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb%2BIP8gHed%2B6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha%2FfJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG%2FXbOFz%2BTcq2MLrcmV28Nmi%2FOHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw%2BluPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs%2B31Wm5qpH%2BiJWSzqdPr2O7zc2TmtnrzCig6bBd%2FvgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I%2FMAp9q9S3Q%2FclADNm1wEeO%2Bxbwg5OIYHZLY3ehG5lJk2xhco%2B6JWybpEVz2wrR6hZyD0QXZbeDVB%2BonmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P%2B1fAll%2BSyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y%2Bcf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY%2Ff%2BucuMtCuP%2Fe4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn%2F%2B7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7%2F6B23%2Bf%2Bsp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz%2FQ0hI0m3NKDRfkgsrnamo%2Bp0CAq1FyvC3a3Nak%2Fs5VX282x9Ufy3E39VAx6o7LpCvO2wK%2Bch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5%2BjP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00%2FWavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR%2BgufwA8GegxUEo4A%2Bgd0BASHiH6jYMLIsUdQJTs%2FC641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW%2BAMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA%2FxUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA%2FE%2BDXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX%2Fk%2F7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh%2FTK18StOPhwG9Xo5mqXAxvw%2F79YQwwDR%2BnAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw%2BoTFqxtz8dxwtogBnF9MjIl%2Fin%2BK8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O%2BGC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6%2FOC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx%2Bv1UKPsfvzQU0Kc83i2VdILmd2%2FyX55zT9luN2%2BCu4nKfwPcK%2FCvDVU%2BpHh8%2BLaldIf1fA5h3ndT6Fln9%2FW%2F9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW%2Fuib%2Fud%2BVK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M%2BVG0KsUdgdvpgP%2F6NqqP0%2FgHZFUfGEijZLHpiIgvV5%2FBltrj8Qd7XQd5p4P%2B7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z%2FvvacG%2Frjpsvnpzs0B1Id6fmX8119l88XnOxe%2FuGrzzHcdu7UtY3%2B2vmXN5zUyj3ZcPl8p1sZSs6%2FnGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q%2Blwg1RdRSpFDNzosb44YJXnAfoEOpZW%2F%2F6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21%2FBWW5DnPQbn2X1WK43%2FaM2n18DfSoybbNHijFpamzXI31eRibGUOxSu%2FlT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO%2FVjC%2FEinToFyoNDz2p9XD6g78WgQdPG7Z3R0T%2FZ5dTM9lsL8Ktek7szl2L%2BgQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T%2F4VeamYPlLtffdQsk%2B2sUTY%2FzDXl%2F05W53%2FBz9UK3p7LjapZ2ZxOm%2BUlZXrL3HHGqO8%2BwVroDaCTTnTxitMxmiAAYQzVJQH%2Bnj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj%2F9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz%2B3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62%2FqN%2FtjQxnSnhnKPw%2BLNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh%2FLvQZnt0evgIhTFV0kE%2FPYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8%2FCj1Y9NY6cnGVW0CjTLaz7E3epvaT%2FLtTV72Rs%2B0WVVmd0dz%2FMGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf%2Fz%2Bfdka%2BeKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ%2Bbewnf18ap4Ib%2BmSMrtDU5zqlD8QANa5MBGh%2FOwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq%2B7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2%2BYuMbmE9dwGYB4KFTsvnDE6Ah47r%2FfE3AYI%2BoXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU%2FA%2FJsw%2BvWDa%2F7emewLaoeibaF1B9qUNnuqWK3%2BUfXYVL1v%2FomD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk%2Bgk8pL0mcCDOn08MDAQlf%2B%2FSlTZ1z12fnqntOhbOTL9%2FZdevbAPN%2Byby1f%2FuUtC%2Fixm8ZBo59LTXEW060hGrTDplNprWd58fwB%2Fb%2FE27BdS%2Fs7U%2BrGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt%2BKh6HN4ti6lxIhT%2FwahnZtWwzlY9QHQ2c79C%2BdxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah%2FPFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn%2B2MnYv36dbMDBTonl%2FT%2Fma5IKAyEGz%2B4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK%2Fyp%2FgLEBVat5WnPDdKA574ubuFJ%2FIUfZ%2FY2Nt6mN%2BZNNTSTaeI56gKwkXerTe9DDHUw8%2FH35FY3nNN7GGuBKWhrV9ep%2B0k1WjNWVaHkW1yA%2BQHWNu8rtBw2a5YXuE40rs7%2FGA%2Bj09V3hA98yRnFPOGr8ltGlsFdD%2F7tRce3LH6Trcneuiy7K7J3khKu%2B3qUaXPWaX7T6%2FKfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM%2B0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz%2BXmcOs1H31U6cC6RTVLfCg7RNBF1UF2%2FwBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH%2BWC0E1qOGtP66s1MYv%2BVLu8%2FXFXvV%2FZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86%2F1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp%2FlYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1%2Bx1SXwLpFi53b%2BaHRYo%2FIrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f%2BnAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g%2FVRdpkkyVpD7jnmax88QwDvg%2F66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb%2BYFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb%2Fd5gqmNaqJZSO1l6PVRy0nZIj%2F45EetjLguh1rLqR%2BSK0hO6NrsqcNX8zoUdjQYDJ7tb4os6%2Bi%2BY0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY%2FgZmE9iuP4V5v7CSR%2FRThaHLh%2BUeBkXwU6BC7lGOevK65udTv%2BtS%2FPfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso%2Fc%2F1K9aLE12fMWLV1y1D9ua8lyJdWXr%2FbG%2BnoCFutf%2FmLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq%2BPEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj%2BnzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3%2B%2BfcnT49Qe%2BkBs%2BwdsMZgPXMp3U5S958snPP%2FEE7bvkOPCuTUDTUQ%2FUzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk%2FseDVWsvDWXXXsyn%2F4wfpXc2V3%2FQxli3jl%2F5hj%2F83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3%2F%2B2TJrzLlkFz%2Bip3qCHKf6eqW02QJLjBYuuj4sobhCWqa%2FYHGEHpcnumuWSOhxeaL7sOakNR6vvmo%2BYcfFA8UFXEPZf9UjyudIOyNwx%2Fi90DdsujS%2FFX2UAwvWSVK4NxaMhAGw3oowp%2Fuc8CTi7D2rBgZWwb%2F60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E%2Bo2t1nePTn8TUmxz%2FA3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD%2BqJopELrl4S6cJNRtEAsLP%2FOdvnJq0Wo0GolY2Et9VFB2Kf%2B4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam%2BbWmUJZfNn5ozKV5Pmmq8PF%2FjVY%2B2Tlk2M2RzSXKjmbQ4RZcQavEYrN%2F9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd%2BSyX%2FCyf0Vxjd2R9NmsunnXYa8xGHzn%2BsSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV%2BD5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H%2FlpZBicglCPN7WOfJkcHqc3ElWqvvz%2F1E6bIQrG%2Btz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw%2BWSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM%2BwEW0El9dM%2FueVRWIsXwe43R7SgbVZqrnqoJ1X%2FkuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM%2BfoaCcwgaoqZUtmodV3p%2BX7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z%2BO4fYIbvZVt4EUcJEKOhHeincWqONw%2Bq6w5Go%2BWGOSR7LhKV%2BKBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx%2BSyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq%2FqlFo1v%2BPTmTi3E%2FglC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO%2BYOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr%2BH1jR7km9vcvqN9657vffefkv%2BfKxge1X%2B7RdjYUPIESN7gTvRkB%2FRMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F%2BUX4%2BWKS4iBTP0IWxGtZgOdMaV4KTayqHQF%2FVihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g%2FqRCuy3vmQ3W9Vu8qbVbP%2BSzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB%2BLopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9RW3t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AF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAyABYRMT%2Fp8RDPn%2BbxQLDPb3DAsUAZD7DBEAAv%2FTAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP%2BnxEM%2Bf5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU%2BAT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR%2BCg8PCvuCCg8PVWQCo%2F3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh%2FdoKDw8KAiYKDw%2F872QDIGT8GGQDIGQETA8K%2B%2BYKDw8KBBoKD2RkZA8K%2FqIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHQRMHRX%2BcBUdHRUBkBUdHRX%2BcBUdHRUBkBUd%2FagdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA%2FAE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3%2BcB0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH%2FoAICLEHFAirBxYB3QI7CAixBxQI%2FPAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI%2FvUBCwgIsggVB%2F70%2FvQHFQiyCAgBC%2F71CAiyCBUHAQwBDAcVBDuzCBUH%2FvT%2B9AcVCLIICAEL%2FvUICLIIFQcBDAEMBxUIsggI%2FvUBDAcAAwAX%2F%2BsExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj%2B1HeOYrSBTU2BAW%2BzmFhYmLOZWFj%2BvJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15%2FtUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF%2F%2FrBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI%2BATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K%2FqIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V%2FnAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P%2Ft6WCg8PCpYKDw%2F%2B3pYKDw8KlgoPD%2F7elgoPDwqWCg8PBLAPCvuCCg8PCgR%2BCg%2F%2BcA8K%2FRIKDw8KAu4KD%2F7UDwr%2BPgoPDwoBwgoPyA8K%2BgoPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ%2FAjY%2FASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ%2BfrJ%2BBJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF%2BfrEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD%2Fu0DwoBEzspASwBLDsp%2FUQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT%2B1PzgKTs7KQMgZP1EArz9RAK8%2FUQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg%2F%2B1A8K%2BgoPrwoFAmoCYAcH%2FaAICv3BCg8PCgF3%2FokKDw8KAj8KAAIAZAAAA%2BgEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V%2FOAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd%2FnAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnL%2BfDIKD68KDw8K%2BgoPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr%2B7Q8KMgoPDwoBXgoPAAAAAAL%2FnAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp%2FeYBr9EVohTQ%2Fp4b4BsBkP5wBLD%2B1AEs%2FnD%2B1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN%2FrkNCxS%2FHQJUMgoPDwr75goPDwoyCg8DhA8EsB0V%2Fj4XEP5wEBABkBAXAcIVHfzgDwr%2BogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K%2Fu0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf%2B7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY%2FAT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX%2FdQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K%2FXQYGf5XDB4CBggEDRkBqRkYAowKDsgOC%2F4%2BCw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJy%2Fov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2%2BDCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ%2BAjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL%2FpYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ%2BAjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L%2FpcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv%2BlwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB%2FAI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA%2FK4KDw8KA1IKDw%2F9CDIKDw8KMgoPD9IBwgoPDwr%2BPgoPD74yCg8PCjIKDw%2FSAcIKDw8K%2Fj4KDw%2B%2BMgoPDwoyCg8P0gHCCg8PCv4%2BCg8PvjIKDw8KMgoPD9IBwgoPDwr%2BPgoPDwSwDwr7ggoPDwoEfgoPyA8K%2FK4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT%2FtRSdmQpOzspA4QpOzsp%2FageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE%2BATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH%2BWwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT%2B%2BuSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF%2F%2FtQKDw8KASx03qP9wAwI%2FjQIDAwIAcwIDAwI%2FjQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ%2FAScmND8BNjIXAUEBAgkMDAn%2B%2FhUZ%2BgoPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg%2BEeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY%2FATYBQQECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK%2FRIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd%2BhGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk%2FnBkAfQCvMjI%2FtTIZAJY%2B7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs%2FUQBLGRkZAOEZGT%2BDGRkAfT%2B1AEsA4RkZGQCWP4MZMgBLAEsyGT%2B1AEs%2FtQBLMhkZGT%2BDP4MAfRk%2FtRkZGRkyGTI%2FtQBLMhkZGT%2B1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI%2FOD%2B1AEsyGRkyGRkASzIyMgD6PwYA%2Bj8GAPo%2FBgD6PwYA%2Bj7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI%2FjAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAwYCqggI%2FjAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg%2F9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj%2F%2FwRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4%2B%2Fj4dBLAdFfuCAbz%2BQwR%2FFR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY%2FATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2%2FkQLEAFOBw45BhcKAcIKFwY%2BDgdTARABVpYKFgROBBYK%2FdoKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw%2F%2Bsf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg%2F%2BcA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP%2FagOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE%2BBAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT%2Bz76Hh76H%2Fu9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K%2F7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE%2FAQEDIQMCqQGBFCgSJQkK%2Fl81LBFS%2Fnk6IgsJKjIe%2FpM4HAwaBwcBj6wBVKIEr%2FwaMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu%2FVoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU%2BATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD%2BDCk7AQYLFyEaAdc5dWM%2BHy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb%2FK5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK%2Fgy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL%2FtQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI%2BAzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI%2FOCnpwMgpwACACH%2FtQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ%2FnAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V%2FagVHR0VA%2BgVHR0V%2FBgVHR0VAyAVHR0V%2FOAVHR0VBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR3dArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V%2FagVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3%2B6WQVHR0VZBUdHf7pZBUdHRVkFR0d%2FulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA%2FAAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG%2F5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V%2FgwVHR3%2B%2BqfIyAHCASwVHR0V%2FtQVHR0VAlgVHR0V%2FagVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V%2FgwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd%2B7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH%2F0SHywsBIT%2B1AEsA%2BgsH%2F12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs%2FNJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7%2B4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4%2BrWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab%2FG8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ%2BBQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ%2Ff4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA%2BhsAgYUFR0OFgoFBmz9BQGQMje7%2FpApOzspAfQpO8i7o%2F5wpbm5Azj%2BlqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp%2FgwpOzsptsj%2BtKW5uaUBkKW5%2Ftf%2BljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml%2FnClubkCfwFTCAj%2BrAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn%2BDCk7OymZZ6W5uaUBkKW5G%2F7TBxUH%2Fs4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX%2BcKW5uQHKAYsHFQdlBwf97QcVB%2FgHB2UHFQdvCBQETBexOyn%2BDCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH%2BAcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE%2BAQgIFAgBBAcFCqrICggBCAgI%2FvgICsiqCgUH%2FvwIFAj%2B%2BAgFCq%2FICgj%2B%2BAgIAQgICsivCgUDlgEICAj%2B%2BAgKyK0KBAf%2B%2FAcVB%2F73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ%2F%2BMB0VZBUdHQRMHRX%2BSgHFDggV%2FBgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ%2F%2BMBUP%2FjAdFWQVHR0ETB0V%2FkoBxQ4IFf5KAcUOCBX8GBUIDgHF%2FkoVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ%2F%2BMBUP%2FhQPDwHsDxUB0A8VBBr8GBUIDgHF%2FkoVCA4B4A4qDgHgDggV%2FkoBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS%2FNISGRkEMv4OCx4L%2Fg4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2%2BsgVHR0VyBUdHQGlyBUdHRXIFR0dA%2BgdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw%2F%2BFA8V%2FjAPFRUPAmQBthUIDv4gDioO%2FiAOCBUBtv47DggVA%2BgVCA4AAAH%2F%2FgACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL%2BMQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX%2BOQ4JFAPoFQkP%2Fj4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA%2BgVCA7%2BOwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW%2B%2B4WCQ8B9Q8q%2FfcD6BUdHRX8GBUdHQQ5%2FeQPFhYPAhwP%2FUgdFWQVHR0VZBUdAAEAiP%2F8A3UESgAFAAAJAgcJAQN1%2FqABYMX92AIoA4T%2Bn%2F6fxgIoAiYAAAAAAQE7%2F%2FwEKARKAAUAAAkBJwkBNwQo%2FdnGAWH%2Bn8YCI%2F3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4%2BrWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX%2BDBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm%2F5%2BHRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI%2FARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ%2BAQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB%2FIHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm%2F4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ%2BAQUiDgMVFDsBFjc%2BATMyFhUUBgciDgUHBhY7ATI%2BAzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ%2FeMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD%2F7UDwoyCg%2FIDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw%2B5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP%2Fs2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA%2FAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJyg2oHB%2F7ACBQIyggIagcVB0%2FFBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf%2BvwcHywcVB2oICE%2FFBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ%2BAQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV%2Ft0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA%2FkAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ%2FAgoQEP32EBf92hUdHRUCJhcD8f5VDSYN%2FlUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3%2B3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN%2FlUNJg3%2BVQ0KFQEjHQSwHRX92hcQ%2FfYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ%2FAgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN%2FlUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I%2FiH%2B1BUdFQ5e%2BQgUCGoHB%2FleDggEsB0V%2FtQVCA5e%2BQcHaggUCPleDhX7UB0VASwVCA5e%2BQcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ%2FAScmNgP2agcH%2BV4OCBX%2B1BUdFQ5e%2BQgU%2FQwBLBUdFQ5e%2BQgUCGoHB%2FleDggEYGoIFAj5Xg4VHRUBLBUIDl75B%2F3xHRX%2B1BUIDl75BwdqCBQI%2BV4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3%2BDA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc%2BBAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP%2FnDI%2FnAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe%2FokKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf%2BiQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc%2BATc%2BAScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy%2Ft3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U%2FM0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc%2BATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY%2BBDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA%2F%2FcAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl%2BDiINfWjIWCwQMWv3%2FIw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP%2FcAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ%2BBTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2%2FVFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP%2FngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U%2FBQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR%2FplkGhPNzRMaZP6ZERgWDwFrBLBYPv6t%2FrsOMRQpFA0M%2Bf75XRRAFRAJgIAJEBVAFF0BB%2FkMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA%2FAEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk%2FHxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd%2FnD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb%2FkPD%2FkOFZ%2F9qP7dFR0dFdECWPEV%2FamNetEVHR0VASMDGvkPD%2FkOFfG1jXqfFQSN5g4qDuYOCBWW%2FagdFWQVHQJYlhUI%2FpiNeh0VZBUd%2Fk3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7%2FtQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3%2B1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6%2BhUdHRX6%2BhUd%2FnD%2B1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB%2F%2F8A1ARMA8IABQAAAQcJAScBBEzG%2Fp%2F%2Bn8UCJwGbxwFh%2Fp%2FHAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH%2Fp8BYQAAAAAC%2F1EAZAVfA%2BgAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW%2FoP%2BHOYOCBWWAYHX%2FRIVHZYVCA7mDioD6B0V%2FdkVDvkPD%2FkOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3%2B1B0qHR8SHQYFyTYUHh4EsBYQoiUY%2FiUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw%2B1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz%2B1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY%2FATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO%2BQ8P%2BQ4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ%2F5Dg75DxUCWBUDYOUPKQ%2FlDwkUl5cUCQ%2FlDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt%2FBgpOzspA%2BgpOzv%2Bq2RkZAGQAtwXLSgV%2FR1kOylkKTs7KWQpO8hkZGRkAAAAA%2F%2BcAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k%2FV0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj%2B7BAIHBUBUQ76AgQQDw36%2FtT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM%2BATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU%2BLwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N%2FrmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI%2BARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW%2Bv4%2BMv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3%2BqBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv%2FtQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo%2FrgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl%2BIZJZkMjIBwvoETCEY%2FqgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX%2BDBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP%2FqoHphwOOmQBJYMGGw%2FLFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7%2BrRUoog0CDRElCkj%2BCVkBUxUoMjIBAgIDBQIZFrdT5B0V%2FgwVHR0VAfQVHQAAAAP%2FnABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf%2Bqg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH%2BJd0PHAaCASZq%2FqoCUGQVHR0VZBUdHQRP5gEFEBEXC%2F3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE%2BAR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN%2FfMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V%2FgwVHR0FDm5UaoMGGw%2F%2BqgemHA4OAWsfFm4NAQxUAS5U1ssVExIW%2FjgCCgIJAwcDBQMBDBGSVv6tZAElgwYb%2FQsdFWQVHR0VZBUdAAP%2F%2FQAGA%2BgFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM%2BBDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX%2BDBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf%2Bqg8bBgHP%2Fq1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob%2B7RAX%2Fu0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm%2F453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX%2B7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm%2BjfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm%2F5AARMQEP7tEBf%2B7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg%2BJFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD%2B7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ%2BASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY%2BATc%2BAzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU%2FOR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg%2FKhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh%2BWYcrIgsW%2FawQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA%2BgpOzsp%2FBgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH%2B1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E%2FtTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv%2BDMj%2BDDspASw7KcgB9Dsp%2FBgpOwSwOylkOyn%2BcGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj%2BL2oHB8deDggV%2FtQVHRUOXscIFALLXg4VHRX%2B1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX%2B1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX%2B1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5%2Fd8t2gXl6gAcbgv29vv%2BC%2Fb2%2F%2BLS0gIC0gAUwtICAWDg83ETNIMykfegEJ%2FoctICAtIAIdLSAgLSAEqF%2Bf3fLdoF5eoN3y3Z9Xb7%2Fgv29vv%2BC%2FBiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf%2FYAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr%2Bqw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB%2F0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH%2F6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6%2BRAH%2FSBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg%2BPqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB%2FslVVsn%2BAeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn%2F8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ%2FARYfAQcXAQKru0KNQjgiHR8uEl%2F3%2FnvUaRONQkIBGxJpCgmNQkL%2B5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl%2F4%2FnvUahKNQrpCARv%2BRmkICY1CukL%2B5UJCjUK7Qjc3LxFf%2BAGFAAAAAAMAyAAAA%2BgEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7%2FVj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8%2FRY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT%2B6dT%2BzP7oywEz0gEsAQsjDx0qBKH%2B5g8qHQ8j%2FvX%2B1NL%2BzcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH%2Fs0NBxMtPGQ%2Bi6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ%2FsoCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc%2BATM2FjMyNxcOAyMiLgEHDgEPASc%2BBTc%2BAScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig%2FIhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn%2F%2FwSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF%2BYOCBWWyJYVCA7mDioBLBYO%2Bg8P%2Bg4WA4QQ%2BQ4V%2FHwDhBUO%2BQ8AAAQAGf%2F%2FA%2BgEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI%2FtTIyAEsArxkZAH0%2FHwWDvoPD%2FoOFgOEZMjI%2FRL6ZJb6ZAAAAAAEABn%2F%2FwPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO%2Bg8P%2Bg4WA4SW%2BmSW%2BmT7UGRkAfRkyMgAAAAEABn%2F%2FwRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD%2FoOFgOE%2FgwBkGT7UGQBkGTIyAAAAAAEABn%2F%2FwRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD%2FoOFgOE%2FgxkAZBkyMj7tAGQZAAAAAAFABn%2F%2FwSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk%2FnABkGT%2BDAH0ASwWDvoPD%2FoOFgOEyMj%2BDMj%2BDMj%2BDMgABQAZ%2F%2F8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD%2BDAH0ZP5wAZBk%2FtQBLGTIyAEsFg76Dw%2F6DhYDhMjI%2FgzI%2FgzI%2FgzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P%2BcKW5uQJn%2FgwpOzspAfQpOzsETLuj%2FnClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml%2FnCju7wCZP4MKTs7KQH0KTs7%2Fm%2F9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn%2BDCk7OykB9Ck7gr4MJAy%2BDAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX%2BcKW5uQJn%2FgwpOzspAfQpOzv%2BFQGQFQsMvgwkDL4MCwRMvKL%2BcKW5uaUBkKO7yDsp%2FgwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf%2BDCk7OykB9Ck7O%2F77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn%2BDCk7OykB9Ck7z%2F0QGBgQ%2FREAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX%2BcBUdHRUBwik7Oyn%2BPhUdHb8BRBAQ%2FrwQFvoVHR0V%2BhYETLml%2FnCluR0VZBUdOykB9Ck7HRVkFR3p%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B%2Fvf%2B8wQMDgkJCQUCUCcn%2FtIXCAoQSwENuwUJEASeCQoRC%2F5TBwEjEv7K%2FsUFDwgLFQnlbm4TFRRWAS%2FTBhAAAAACAAAAAAT%2BBEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4%2BKTs7KQHCFR0dFf5wpbm5AvEBRBAQ%2FrwQFvoVHR0V%2BhYETB0VZBUdOyn%2BDCk7HRVkFR25pQGQpbnp%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj%2B75wpOzspAfQpO8i7o%2F5wpbm5pQEsBLAdFf5wFQgObf7IDw%2BNDykPAThtDhX%2B1Dsp%2FgwpOzsplMj%2B1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx%2FnugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO%2FoEOJw3%2BhQ0JFfod%2FoUD6BUd%2B7QdA2dkBLAdFf6iFg%2F%2BVg8PAaoPFgFeFR38fB0V%2BvoVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE%2BAQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n%2FfkD6BUd%2B7QdA2dkBJ7%2BTQ8g%2BhQeHRX6IQ8BrxAC%2FH8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB%2F7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH%2FfMICAEjCBQIiwcH%2FdIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY%2FASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A%2BgVHfu0HQNnZAP%2Fjg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V%2BvoVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ%2FAQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A%2BgVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3%2BWB0V%2BvoVHWQyMgACAAT%2F7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d%2Fsb%2BcQSs%2FTMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V%2BgH0ZMhkZAPo%2FK4VHQGQ%2FnAdFQPoFB7%2B1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC%2FtVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf%2BXAgUCMoICGoHFQdPASkHFQPolf7VXwkJk%2F5wHRUD6BQe%2FtQBLMjI%2Fc5qBxUH%2FlsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72%2FreWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykD6P7zZ4YPAw7%2B9v5wHRUD6BQe%2FtQBLMjI%2FYxGDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM%2FqIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA%2Bj%2B1B0Vlv5wHRUD6BQe%2FtQBLMjI%2FagdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75%2Fm6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P%2Fvf%2BcB0VA%2BgUHv7UASzIyP2F5Q8V%2BhQeHhT6FQ%2FlDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd%2B1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U%2FdoVHR0VAib%2B1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX%2BcAGQFf0vMhUdHRUyyGRk%2FoL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI%2FoUdFWQVHcjI%2FcvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ%2BAzMyFh8BETMRPAE%2BAjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX%2B4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH%2FuuFR0dFQGNAQgbHzUeAWcfRJEZDA3%2BPhw%2FMSkLC%2F5BFR0dFQG%2FBA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr%2BdiUNODgBBQoUDgN4FhkBAjg4DSX%2BdgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn%2B2cj%2BcGQBd%2F4lASwpOzsp%2FtQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn%2BDCk7OykB9Ck7ZMjI%2FveFo4XGyMhm%2BBUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE%2BBRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB%2FLimdWQ%2FLAkJHRTKFB2N%2FsKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V%2B7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy%2FtEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI%2FOAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7%2FRv%2B1DspZCk7BEw7KfwYA%2BgpO%2F7UOyn9RAK8KTv84AGQKTs7KQAAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkyMhkZARMsHz%2BDHywsHwB9HywyP1EArzIZP7UZGQBLGT%2B1GQB9GT%2B1AEsAAAABf%2BcAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE%2FgxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI%2FUQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP%2BcAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj%2B1AEsAZDIyP7UASwETLB8%2Fgx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP%2BcAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE%2Fgz%2B1AEsAZD%2B1ARMsHz%2BDHywsHwB9HywyP1EArz%2BDJaWlpYBLAAAAAX%2FnAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8%2Fgx8sLB8AfR8sMj9RAK8%2FagB9GRWQUFUASz%2B1FRBQVYAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkZMjIZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yGT%2B1GRkASz%2BDAGQZP4MZAAG%2F5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8%2FUR8sLAYA4T9RMj%2B1GQCWGRkyP2oZAEsZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yP5wAfT%2BDAGQZMjIyP7UZAAF%2F5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI%2FUQCvP5wyDLIlmT%2BDAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz%2B1AEs%2FtRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv%2Fy%2F50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V%2FtQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi%2FbMPCv7tFxD0EBD0EBcBEwoPAAAAAAL%2F8v%2BcBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr%2Bhg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ%2BteGOkHAFoEBD%2Bk3NPRWsOExNkqWP9kuQQF%2F7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD%2B8qr%2B8qr%2B1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3%2Bs%2F7U%2FtRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd%2FtQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd%2Bx5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc%2BATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ%2Fj0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb%2F7V%2FtVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s%2FoQPCgI%2FASzIZKLU1KJktP51Cg8DhA8KwwMg%2FoTIyALzCg%2F%2B1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz%2Bov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9%2BAFe%2FdoERwoP%2FtTI%2FHzIyNT%2BZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT%2BogHCJCsRBP5w%2FnAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX%2B1BUdHRUyHQFzKTs7Kf2oARP2%2Fro7KVg%2BASw%2BWP201MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7KfzgKTsE9gFGAUQpO5Y%2BWFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG%2F2oKTs7KVg%2BASw%2BWAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7Kf4M%2FtQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP%2FogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs%2BwQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf%2BTB0U%2FtIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K%2BgoPDwr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPDwRMDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg%2F%2B1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko%2FPwoKUIcMT4BXmRkyP4%2BArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3%2B1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq%2F9WrIiCikBSAOvArwVHR0VlgET%2FEoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb%2BlR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf%2BvCcrI%2F7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW%2Fus5Okxra0w6Of5yHSodZGQdKh3%2B1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ%2F68Jysj%2Fs0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2%2BjGv4KOTpMa2tMOjn%2Bch0qHWRkHSod%2FtQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ%2BAjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz%2B1CU%2FP00T%2Fe48PUJtj0r%2BogK8FR0dFQ2K%2FEqJDBUdHbMETBUd%2B1AdBLChizlmUT9IGVO9VFShdksE%2FH4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA%2BgFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U%2F5wU3V1BRQ7KU4aXDYyMjZcGk4p%2Fkc2LmRkZGRkU3V1UwGQU3UAAAMAZP%2F%2FBEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr%2B5eANGiUNWP30Vw0mGg3g%2Ft8KDw8BqmQBRGQETB0V%2FgwVHR0VAfQVHf1EDwoyCg%2FgDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn%2B1AEsKTv%2BDDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg%2F4f%2FfsgIAEnH1n%2BrAFWAS%2F%2Bq6IDIBUd%2FHwdBI39VR9ZH4MCBh9ZHwEoH%2F5u%2FqoBMAFV%2FBsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV%2FnAVJwi9CBMVAnEdKh19FROo%2Fa0dFTIdFTDILxUdMhUdAocB%2BhMcHBP%2BBhMclhUdHRWWHP2MMhUdMhUdASz%2B1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr%2Bif7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD%2F7UyP4M%2FcEKDw8KA7YKD%2F7UyAAC%2F5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc%2BARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF%2BgoPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb%2FnP%2FmBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL%2FCtqQ0geZgM3FR0cE%2F0fFyIJKjr%2B1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g%2ByD5YrBwVODMQEAtEERzJLUAtLUD%2B24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz%2B%2BQFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg%2F%2B1MgVHR0LCgQOIhoW%2FnDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb%2BYDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj%2FsIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn%2BPilUrAFINhWWFTYFFKxn%2FgwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn%2BPilUrAFINhWWFTYFFJaz%2FkIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO%2FYL%2Bhk5klpZkAQEBBQQvkwKCAVz%2Bov6iAV4BXgL%2F%2FuWqPOCWx5SVyJb6BA0GCgYDKEEBG%2F1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz%2ByFh8WFh8%2FshYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o%2F4Mpbm5Asv9qCk7OykCWCk7O%2F2xAfQVHR0V%2FgwVHR1HAZAEsLuj%2FgylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX%2B1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA%2BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0V%2FBgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA%2BgEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8%2FV1c%2FP%2F75fLB8sAK8sHyw%2FcB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy%2FsCwfLB8fLB8sP7UsHywfHywfLD%2B05AVHR0VjgQAAAH%2FtQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8IAQQNGyc%2FKZK4ChRUFQu4jjBJJxkHAgcPGQYGCAsBKhQaTBQVCiMUM7YDe7YsFCMKFgNuEwYS%2FtkLHw8OEw0dNkY4MhwBIBgXBAQYF%2F7gKjxTQyMNEw4PHwoBKBIHEwUjKBYGDMHBDAUWKCMAAAAAAgAAAAAEsASwACUAQwAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjNC4DKwERFBYXMxUjNTI1ESMiDgMVIzUhBLAyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj9RBkIChgQEWQZDQzIMmQREBgKCBkB9AOEFSAVDggDAfyuFhkBAmRkAQUJFQ4DUgEDCA4VIBUBLP0SDxMKBQH%2BVwsNATIyGQGpAQUKEw%2BWAAAAAAMAAAAABEwErgAdACAAMAAAATUiJy4BLwEBIwEGBw4BDwEVITUiJj8BIRcWBiMVARsBARUUBiMhIiY9ATQ2MyEyFgPoGR4OFgUE%2Ft9F%2FtQSFQkfCwsBETE7EkUBJT0NISf%2B7IZ5AbEdFfwYFR0dFQPoFR0BLDIgDiIKCwLr%2FQ4jFQkTBQUyMisusKYiQTIBhwFW%2Fqr942QVHR0VZBUdHQADAAAAAASwBLAADwBHAEoAABMhMhYVERQGIyEiJjURNDYFIyIHAQYHBgcGHQEUFjMhMjY9ATQmIyInJj8BIRcWBwYjIgYdARQWMyEyNj0BNCYnIicmJyMBJhMjEzIETBUdHRX7tBUdHQJGRg0F%2FtUREhImDAsJAREIDAwINxAKCj8BCjkLEQwYCAwMCAE5CAwLCBEZGQ8B%2FuAFDsVnBLAdFfu0FR0dFQRMFR1SDP0PIBMSEAUNMggMDAgyCAwXDhmjmR8YEQwIMggMDAgyBwwBGRskAuwM%2FgUBCAAABAAAAAAEsASwAAMAEwAjACcAAAEhNSEFITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NhcRIREEsPtQBLD7ggGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHUcBLARMZMgdFfx8FR0dFQOEFR0dFf5wFR0dFQGQFR1k%2FtQBLAAEAAAAAASwBLAADwAfACMAJwAAEyEyFhURFAYjISImNRE0NgEhMhYVERQGIyEiJjURNDYXESEREyE1ITIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR1HASzI%2B1AEsASwHRX8fBUdHRUDhBUd%2FgwdFf5wFR0dFQGQFR1k%2FtQBLP2oZAAAAAACAAAAZASwA%2BgAJwArAAATITIWFREzNTQ2MyEyFh0BMxUjFRQGIyEiJj0BIxEUBiMhIiY1ETQ2AREhETIBkBUdZB0VAZAVHWRkHRX%2BcBUdZB0V%2FnAVHR0CnwEsA%2BgdFf6ilhUdHRWWZJYVHR0Vlv6iFR0dFQMgFR3%2B1P7UASwAAAQAAAAABLAEsAADABMAFwAnAAAzIxEzFyEyFhURFAYjISImNRE0NhcRIREBITIWFREUBiMhIiY1ETQ2ZGRklgGQFR0dFf5wFR0dRwEs%2FqIDhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAAAAgBkAAAETASwACcAKwAAATMyFhURFAYrARUhMhYVERQGIyEiJjURNDYzITUjIiY1ETQ2OwE1MwcRIRECWJYVHR0VlgHCFR0dFfx8FR0dFQFelhUdHRWWZMgBLARMHRX%2BcBUdZB0V%2FnAVHR0VAZAVHWQdFQGQFR1kyP7UASwAAAAEAAAAAASwBLAAAwATABcAJwAAISMRMwUhMhYVERQGIyEiJjURNDYXESERASEyFhURFAYjISImNRE0NgSwZGT9dgGQFR0dFf5wFR0dRwEs%2FK4DhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAEBLAAwA28EgAAPAAAJAQYjIiY1ETQ2MzIXARYUA2H%2BEhcSDhAQDhIXAe4OAjX%2BEhcbGQPoGRsX%2FhIOKgAAAAABAUEAMgOEBH4ACwAACQE2FhURFAYnASY0AU8B7h0qKh3%2BEg4CewHuHREp%2FBgpER0B7g4qAAAAAAEAMgFBBH4DhAALAAATITIWBwEGIicBJjZkA%2BgpER3%2BEg4qDv4SHREDhCod%2FhIODgHuHSoAAAAAAQAyASwEfgNvAAsAAAkBFgYjISImNwE2MgJ7Ae4dESn8GCkRHQHuDioDYf4SHSoqHQHuDgAAAAACAAgAAASwBCgABgAKAAABFQE1LQE1ASE1IQK8%2FUwBnf5jBKj84AMgAuW2%2Fr3dwcHd%2B9jIAAAAAAIAAABkBLAEsAALADEAAAEjFTMVIREzNSM1IQEzND4FOwERFAYPARUhNSIuAzURMzIeBRUzESEEsMjI%2FtTIyAEs%2B1AyCAsZEyYYGWQyGRkBkAQOIhoWZBkYJhMZCwgy%2FOADhGRkASxkZP4MFSAVDggDAf3aFhkBAmRkAQUJFQ4CJgEDCA4VIBUBLAAAAgAAAAAETAPoACUAMQAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjFTMVIREzNSM1IQMgMggLGRMmGBlkMhkZ%2FnAEDiIaFmQZGCYTGQsIMgMgASzIyP7UyMgBLAK8FSAVDggDAf3aFhkCAWRkAQUJFQ4CJgEDCA4VIBUBLPzgZGQBLGRkAAABAMgAZgNyBEoAEgAAATMyFgcJARYGKwEiJwEmNDcBNgK9oBAKDP4wAdAMChCgDQr%2BKQcHAdcKBEoWDP4w%2FjAMFgkB1wgUCAHXCQAAAQE%2BAGYD6ARKABIAAAEzMhcBFhQHAQYrASImNwkBJjYBU6ANCgHXBwf%2BKQoNoBAKDAHQ%2FjAMCgRKCf4pCBQI%2FikJFgwB0AHQDBYAAAEAZgDIBEoDcgASAAAAFh0BFAcBBiInASY9ATQ2FwkBBDQWCf4pCBQI%2FikJFgwB0AHQA3cKEKANCv4pBwcB1woNoBAKDP4wAdAAAAABAGYBPgRKA%2BgAEgAACQEWHQEUBicJAQYmPQE0NwE2MgJqAdcJFgz%2BMP4wDBYJAdcIFAPh%2FikKDaAQCgwB0P4wDAoQoA0KAdcHAAAAAgDZ%2F%2FkEPQSwAAUAOgAAARQGIzQ2BTMyFh8BNjc%2BAh4EBgcOBgcGIiYjIgYiJy4DLwEuAT4EHgEXJyY2A%2BiwfLD%2BVmQVJgdPBQsiKFAzRyorDwURAQQSFyozTSwNOkkLDkc3EDlfNyYHBw8GDyUqPjdGMR%2BTDA0EsHywfLDIHBPCAQIGBwcFDx81S21DBxlLR1xKQhEFBQcHGWt0bCQjP2hJNyATBwMGBcASGAAAAAACAMgAFQOEBLAAFgAaAAATITIWFREUBisBEQcGJjURIyImNRE0NhcVITX6AlgVHR0Vlv8TGpYVHR2rASwEsB0V%2FnAVHf4MsgkQFQKKHRUBkBUdZGRkAAAAAgDIABkETASwAA4AEgAAEyEyFhURBRElIREjETQ2ARU3NfoC7ic9%2FUQCWP1EZB8BDWQEsFEs%2FFt1A7Z9%2FBgEARc0%2FV1kFGQAAQAAAAECTW%2FDBF9fDzz1AB8EsAAAAADQdnOXAAAAANB2c5f%2FUf%2BcBdwFFAAAAAgAAg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3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzMyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI1IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTYiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyNmZhOyIgZD0iTTc3NCAxMTkzLjVxMTYgLTkuNSAyMC41IC0yN3QtNS41IC0zMy41bC0xMzYgLTE4N2w0NjcgLTc0NmgzMHEyMCAwIDM1IC0xOC41dDE1IC0zOS41di00MmgtMTIwMHY0MnEwIDIxIDE1IDM5LjV0MzUgMTguNWgzMGw0NjggNzQ2bC0xMzUgMTgzcS0xMCAxNiAtNS41IDM0dDIwLjUgMjh0MzQgNS41dDI4IC0yMC41bDExMSAtMTQ4bDExMiAxNTBxOSAxNiAyNyAyMC41dDM0IC01ek02MDAgMjAwaDM3N2wtMTgyIDExMmwtMTk1IDUzNHYtNjQ2eiAiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI0OyIgZD0iTTEzMDAgMGgtNTM4bC00MSA0MDBoLTI0MmwtNDEgLTQwMGgtNTM4bDQzMSAxMjAwaDIwOWwtMjEgLTMwMGgxNjJsLTIwIDMwMGgyMDh6TTUxNSA4MDBsLTI3IC0zMDBoMjI0bC0yNyAzMDBoLTE3MHoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI5OyIgZD0iTTYwMCAxMTc3cTExNyAwIDIyNCAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI0dC00NS41IC0yMjR0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNCAtNDUuNXQtMjI0IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHQ0NS41IDIyNHQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNCA0NS41ek02MDAgMTAyN3EtMTE2IDAgLTIxNC41IC01N3QtMTU1LjUgLTE1NS41dC01NyAtMjE0LjV0NTcgLTIxNC41IHQxNTUuNSAtMTU1LjV0MjE0LjUgLTU3dDIxNC41IDU3dDE1NS41IDE1NS41dDU3IDIxNC41dC01NyAyMTQuNXQtMTU1LjUgMTU1LjV0LTIxNC41IDU3ek01NDAgODIwbDI1MyAtMTkwcTE3IC0xMiAxNyAtMzB0LTE3IC0zMGwtMjUzIC0xOTBxLTE2IC0xMiAtMjggLTYuNXQtMTIgMjYuNXY0MDBxMCAyMSAxMiAyNi41dDI4IC02LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzMDsiIGQ9Ik05NDcgMTA2MGwxMzUgMTM1cTcgNyAxMi41IDV0NS41IC0xM3YtMzYycTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtMzYycS0xMSAwIC0xMyA1LjV0NSAxMi41bDEzMyAxMzNxLTEwOSA3NiAtMjM4IDc2cS0xMTYgMCAtMjE0LjUgLTU3dC0xNTUuNSAtMTU1LjV0LTU3IC0yMTQuNXQ1NyAtMjE0LjV0MTU1LjUgLTE1NS41dDIxNC41IC01N3QyMTQuNSA1N3QxNTUuNSAxNTUuNXQ1NyAyMTQuNWgxNTBxMCAtMTE3IC00NS41IC0yMjQgdC0xMjMgLTE4NC41dC0xODQuNSAtMTIzdC0yMjQgLTQ1LjV0LTIyNCA0NS41dC0xODQuNSAxMjN0LTEyMyAxODQuNXQtNDUuNSAyMjR0NDUuNSAyMjR0MTIzIDE4NC41dDE4NC41IDEyM3QyMjQgNDUuNXExOTIgMCAzNDcgLTExN3oiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQyOyIgZD0iTTUwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTY4MiA2ODJsMSA0NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWg0NzR6TTgwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTU2IDU2bDQyNCA0MjZsLTcwMCA3MDBoMTUwek0zMTkuNSAxMDI0LjVxLTI5LjUgMjkuNSAtNzEgMjkuNXQtNzEgLTI5LjUgdC0yOS41IC03MS41dDI5LjUgLTcxLjV0NzEgLTI5LjV0NzEgMjkuNXQyOS41IDcxLjV0LTI5LjUgNzEuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQzOyIgZD0iTTMwMCAxMjAwaDgyNXE3NSAwIDc1IC03NXYtOTAwcTAgLTI1IC0xOCAtNDNsLTY0IC02NHEtOCAtOCAtMTMgLTUuNXQtNSAxMi41djk1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjVoLTcwMHEtMjUgMCAtNDMgLTE4bC02NCAtNjRxLTggLTggLTUuNSAtMTN0MTIuNSAtNWg3MDBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di05NTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC04NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY5NzUgcTAgMjUgMTggNDNsMTM5IDEzOXExOCAxOCA0MyAxOHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ5OyIgZD0iTTg3NyAxMjAwbDIgLTU3cS04MyAtMTkgLTExNiAtNDUuNXQtNDAgLTY2LjVsLTEzMiAtODM5cS05IC00OSAxMyAtNjl0OTYgLTI2di05N2gtNTAwdjk3cTE4NiAxNiAyMDAgOThsMTczIDgzMnEzIDE3IDMgMzB0LTEuNSAyMi41dC05IDE3LjV0LTEzLjUgMTIuNXQtMjEuNSAxMHQtMjYgOC41dC0zMy41IDEwcS0xMyAzIC0xOSA1djU3aDQyNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDUwOyIgZD0iTTEzMDAgOTAwaC01MHEwIDIxIC00IDM3dC05LjUgMjYuNXQtMTggMTcuNXQtMjIgMTF0LTI4LjUgNS41dC0zMSAydC0zNyAwLjVoLTIwMHYtODUwcTAgLTIyIDI1IC0zNC41dDUwIC0xMy41bDI1IC0ydi0xMDBoLTQwMHYxMDBxNCAwIDExIDAuNXQyNCAzdDMwIDd0MjQgMTV0MTEgMjQuNXY4NTBoLTIwMHEtMjUgMCAtMzcgLTAuNXQtMzEgLTJ0LTI4LjUgLTUuNXQtMjIgLTExdC0xOCAtMTcuNXQtOS41IC0yNi41dC00IC0zN2gtNTB2MzAwIGgxMDAwdi0zMDB6TTE3NSAxMDAwaC03NXYtODAwaDc1bC0xMjUgLTE2N2wtMTI1IDE2N2g3NXY4MDBoLTc1bDEyNSAxNjd6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA1MTsiIGQ9Ik0xMTAwIDkwMGgtNTBxMCAyMSAtNCAzN3QtOS41IDI2LjV0LTE4IDE3LjV0LTIyIDExdC0yOC41IDUuNXQtMzEgMnQtMzcgMC41aC0yMDB2LTY1MHEwIC0yMiAyNSAtMzQuNXQ1MCAtMTMuNWwyNSAtMnYtMTAwaC00MDB2MTAwcTQgMCAxMSAwLjV0MjQgM3QzMCA3dDI0IDE1dDExIDI0LjV2NjUwaC0yMDBxLTI1IDAgLTM3IC0wLjV0LTMxIC0ydC0yOC41IC01LjV0LTIyIC0xMXQtMTggLTE3LjV0LTkuNSAtMjYuNXQtNCAtMzdoLTUwdjMwMCBoMTAwMHYtMzAwek0xMTY3IDUwbC0xNjcgLTEyNXY3NWgtODAwdi03NWwtMTY3IDEyNWwxNjcgMTI1di03NWg4MDB2NzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA1MjsiIGQ9Ik01MCAxMTAwaDYwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMTAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC02MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djEwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTUwIDgwMGgxMDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0xMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEwMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djEwMCBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek01MCA1MDBoODAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0xMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTgwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MTAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNNTAgMjAwaDExMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTEwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTEwMCBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djEwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA1MzsiIGQ9Ik0yNTAgMTEwMGg3MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTEwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNzAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek01MCA4MDBoMTEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMTAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDAgcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNMjUwIDUwMGg3MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTEwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNzAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek01MCAyMDBoMTEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMTAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMTAwIHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MTAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDY5OyIgZD0iTTI1MCAxMTAwaDEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDM4bDQ2NCA0NTNxMTUgMTQgMjUuNSAxMHQxMC41IC0yNXYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcwOyIgZD0iTTUwIDExMDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di0xMDAwcTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNSB0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEyMDAgMTA1MHYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ5MiA0ODBxLTE1IDE0IC0xNSAzNXQxNSAzNWw0OTIgNDgwcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV2LTQzOGw0NjQgNDUzcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3MjsiIGQ9Ik0yNDMgMTA3NGw4MTQgLTQ5OHExOCAtMTEgMTggLTI2dC0xOCAtMjZsLTgxNCAtNDk4cS0xOCAtMTEgLTMwLjUgLTR0LTEyLjUgMjh2MTAwMHEwIDIxIDEyLjUgMjh0MzAuNSAtNHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc1OyIgZD0iTTUwMCA2MTJ2NDM4cTAgMjEgMTAuNSAyNXQyNS41IC0xMGw0OTIgLTQ4MHExNSAtMTQgMTUgLTM1dC0xNSAtMzVsLTQ5MiAtNDgwcS0xNSAtMTQgLTI1LjUgLTEwdC0xMC41IDI1djQzOGwtNDY0IC00NTNxLTE1IC0xNCAtMjUuNSAtMTB0LTEwLjUgMjV2MTAwMHEwIDIxIDEwLjUgMjV0MjUuNSAtMTB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3NjsiIGQ9Ik0xMDQ4IDExMDJsMTAwIDFxMjAgMCAzNSAtMTQuNXQxNSAtMzUuNWw1IC0xMDAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41bC0xMDAgLTFxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41bC0yIDQzN2wtNDYzIC00NTRxLTE0IC0xNSAtMjQuNSAtMTAuNXQtMTAuNSAyNS41bC0yIDQzN2wtNDYyIC00NTVxLTE1IC0xNCAtMjUuNSAtOS41dC0xMC41IDI0LjVsLTUgMTAwMHEwIDIxIDEwLjUgMjUuNXQyNS41IC0xMC41bDQ2NiAtNDUwIGwtMiA0MzhxMCAyMCAxMC41IDI0LjV0MjUuNSAtOS41bDQ2NiAtNDUxbC0yIDQzOHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3NzsiIGQ9Ik04NTAgMTEwMGgxMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTEwMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2NDM4bC00NjQgLTQ1M3EtMTUgLTE0IC0yNS41IC0xMHQtMTAuNSAyNXYxMDAwcTAgMjEgMTAuNSAyNXQyNS41IC0xMGw0NjQgLTQ1M3Y0MzhxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwNzg7IiBkPSJNNjg2IDEwODFsNTAxIC01NDBxMTUgLTE1IDEwLjUgLTI2dC0yNi41IC0xMWgtMTA0MnEtMjIgMCAtMjYuNSAxMXQxMC41IDI2bDUwMSA1NDBxMTUgMTUgMzYgMTV0MzYgLTE1ek0xNTAgNDAwaDEwMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTEwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTAwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MTAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc5OyIgZD0iTTg4NSA5MDBsLTM1MiAtMzUzbDM1MiAtMzUzbC0xOTcgLTE5OGwtNTUyIDU1Mmw1NTIgNTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODA7IiBkPSJNMTA2NCA1NDdsLTU1MSAtNTUxbC0xOTggMTk4bDM1MyAzNTNsLTM1MyAzNTNsMTk4IDE5OHoiIC8%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk2OyIgZD0iTTg1MCAxMjAwaDMwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMzAwcTAgLTIxIC0xMC41IC0yNXQtMjQuNSAxMGwtOTQgOTRsLTI0OSAtMjQ5cS04IC03IC0xOCAtN3QtMTggN2wtMTA2IDEwNnEtNyA4IC03IDE4dDcgMThsMjQ5IDI0OWwtOTQgOTRxLTE0IDE0IC0xMCAyNC41dDI1IDEwLjV6TTM1MCAwaC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djMwMHEwIDIxIDEwLjUgMjV0MjQuNSAtMTBsOTQgLTk0bDI0OSAyNDkgcTggNyAxOCA3dDE4IC03bDEwNiAtMTA2cTcgLTggNyAtMTh0LTcgLTE4bC0yNDkgLTI0OWw5NCAtOTRxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNXoiIC8%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTYyOyIgZD0iTTc5MyAxMTgybDkgLTlxOCAtMTAgNSAtMjdxLTMgLTExIC03OSAtMjI1LjV0LTc4IC0yMjEuNWwzMDAgMXEyNCAwIDMyLjUgLTE3LjV0LTUuNSAtMzUuNXEtMSAwIC0xMzMuNSAtMTU1dC0yNjcgLTMxMi41dC0xMzguNSAtMTYyLjVxLTEyIC0xNSAtMjYgLTE1aC05bC05IDhxLTkgMTEgLTQgMzJxMiA5IDQyIDEyMy41dDc5IDIyNC41bDM5IDExMGgtMzAycS0yMyAwIC0zMSAxOXEtMTAgMjEgNiA0MXE3NSA4NiAyMDkuNSAyMzcuNSB0MjI4IDI1N3Q5OC41IDExMS41cTkgMTYgMjUgMTZoOXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY1OyIgZD0iTTYwMCAxMTg2cTExOSAwIDIyNy41IC00Ni41dDE4NyAtMTI1dDEyNSAtMTg3dDQ2LjUgLTIyNy41dC00Ni41IC0yMjcuNXQtMTI1IC0xODd0LTE4NyAtMTI1dC0yMjcuNSAtNDYuNXQtMjI3LjUgNDYuNXQtMTg3IDEyNXQtMTI1IDE4N3QtNDYuNSAyMjcuNXQ0Ni41IDIyNy41dDEyNSAxODd0MTg3IDEyNXQyMjcuNSA0Ni41ek02MDAgMTAyMnEtMTE1IDAgLTIxMiAtNTYuNXQtMTUzLjUgLTE1My41dC01Ni41IC0yMTJ0NTYuNSAtMjEyIHQxNTMuNSAtMTUzLjV0MjEyIC01Ni41dDIxMiA1Ni41dDE1My41IDE1My41dDU2LjUgMjEydC01Ni41IDIxMnQtMTUzLjUgMTUzLjV0LTIxMiA1Ni41ek02MDAgNzk0cTgwIDAgMTM3IC01N3Q1NyAtMTM3dC01NyAtMTM3dC0xMzcgLTU3dC0xMzcgNTd0LTU3IDEzN3Q1NyAxMzd0MTM3IDU3eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNjY7IiBkPSJNNDUwIDEyMDBoMjAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0zNTBoMjQ1cTIwIDAgMjUgLTExdC05IC0yNmwtMzgzIC00MjZxLTE0IC0xNSAtMzMuNSAtMTV0LTMyLjUgMTVsLTM3OSA0MjZxLTEzIDE1IC04LjUgMjZ0MjUuNSAxMWgyNTB2MzUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNNTAgMzAwaDEwMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTI1MGgtMTEwMHYyNTBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41eiBNOTAwIDIwMHYtNTBoMTAwdjUwaC0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE2NzsiIGQ9Ik01ODMgMTE4MmwzNzggLTQzNXExNCAtMTUgOSAtMzF0LTI2IC0xNmgtMjQ0di0yNTBxMCAtMjAgLTE3IC0zNXQtMzkgLTE1aC0yMDBxLTIwIDAgLTMyIDE0LjV0LTEyIDM1LjV2MjUwaC0yNTBxLTIwIDAgLTI1LjUgMTYuNXQ4LjUgMzEuNWwzODMgNDMxcTE0IDE2IDMzLjUgMTd0MzMuNSAtMTR6TTUwIDMwMGgxMDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yNTBoLTExMDB2MjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXogTTkwMCAyMDB2LTUwaDEwMHY1MGgtMTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNjg7IiBkPSJNMzk2IDcyM2wzNjkgMzY5cTcgNyAxNy41IDd0MTcuNSAtN2wxMzkgLTEzOXE3IC04IDcgLTE4LjV0LTcgLTE3LjVsLTUyNSAtNTI1cS03IC04IC0xNy41IC04dC0xNy41IDhsLTI5MiAyOTFxLTcgOCAtNyAxOHQ3IDE4bDEzOSAxMzlxOCA3IDE4LjUgN3QxNy41IC03ek01MCAzMDBoMTAwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjUwaC0xMTAwdjI1MHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTkwMCAyMDB2LTUwaDEwMHY1MCBoLTEwMHoiIC8%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTk5OyIgZD0iTTcwMCAxMDB2MTAwaDQwMGwtMjcwIDMwMGgxNzBsLTI3MCAzMDBoMTcwbC0zMDAgMzMzbC0zMDAgLTMzM2gxNzBsLTI3MCAtMzAwaDE3MGwtMjcwIC0zMDBoNDAwdi0xMDBoLTUwcS0yMSAwIC0zNS41IC0xNC41dC0xNC41IC0zNS41di01MGg0MDB2NTBxMCAyMSAtMTQuNSAzNS41dC0zNS41IDE0LjVoLTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMDA7IiBkPSJNNjAwIDExNzlxOTQgMCAxNjcuNSAtNTYuNXQ5OS41IC0xNDUuNXE4OSAtNiAxNTAuNSAtNzEuNXQ2MS41IC0xNTUuNXEwIC02MSAtMjkuNSAtMTEyLjV0LTc5LjUgLTgyLjVxOSAtMjkgOSAtNTVxMCAtNzQgLTUyLjUgLTEyNi41dC0xMjYuNSAtNTIuNXEtNTUgMCAtMTAwIDMwdi0yNTFxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTUwaC0zMDB2NTBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41djI1MXEtNDUgLTMwIC0xMDAgLTMwIHEtNzQgMCAtMTI2LjUgNTIuNXQtNTIuNSAxMjYuNXEwIDE4IDQgMzhxLTQ3IDIxIC03NS41IDY1dC0yOC41IDk3cTAgNzQgNTIuNSAxMjYuNXQxMjYuNSA1Mi41cTUgMCAyMyAtMnEwIDIgLTEgMTB0LTEgMTNxMCAxMTYgODEuNSAxOTcuNXQxOTcuNSA4MS41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMDE7IiBkPSJNMTAxMCAxMDEwcTExMSAtMTExIDE1MC41IC0yNjAuNXQwIC0yOTl0LTE1MC41IC0yNjAuNXEtODMgLTgzIC0xOTEuNSAtMTI2LjV0LTIxOC41IC00My41dC0yMTguNSA0My41dC0xOTEuNSAxMjYuNXEtMTExIDExMSAtMTUwLjUgMjYwLjV0MCAyOTl0MTUwLjUgMjYwLjVxODMgODMgMTkxLjUgMTI2LjV0MjE4LjUgNDMuNXQyMTguNSAtNDMuNXQxOTEuNSAtMTI2LjV6TTQ3NiAxMDY1cS00IDAgLTggLTFxLTEyMSAtMzQgLTIwOS41IC0xMjIuNSB0LTEyMi41IC0yMDkuNXEtNCAtMTIgMi41IC0yM3QxOC41IC0xNGwzNiAtOXEzIC0xIDcgLTFxMjMgMCAyOSAyMnEyNyA5NiA5OCAxNjZxNzAgNzEgMTY2IDk4cTExIDMgMTcuNSAxMy41dDMuNSAyMi41bC05IDM1cS0zIDEzIC0xNCAxOXEtNyA0IC0xNSA0ek01MTIgOTIwcS00IDAgLTkgLTJxLTgwIC0yNCAtMTM4LjUgLTgyLjV0LTgyLjUgLTEzOC41cS00IC0xMyAyIC0yNHQxOSAtMTRsMzQgLTlxNCAtMSA4IC0xcTIyIDAgMjggMjEgcTE4IDU4IDU4LjUgOTguNXQ5Ny41IDU4LjVxMTIgMyAxOCAxMy41dDMgMjEuNWwtOSAzNXEtMyAxMiAtMTQgMTlxLTcgNCAtMTUgNHpNNzE5LjUgNzE5LjVxLTQ5LjUgNDkuNSAtMTE5LjUgNDkuNXQtMTE5LjUgLTQ5LjV0LTQ5LjUgLTExOS41dDQ5LjUgLTExOS41dDExOS41IC00OS41dDExOS41IDQ5LjV0NDkuNSAxMTkuNXQtNDkuNSAxMTkuNXpNODU1IDU1MXEtMjIgMCAtMjggLTIxcS0xOCAtNTggLTU4LjUgLTk4LjV0LTk4LjUgLTU3LjUgcS0xMSAtNCAtMTcgLTE0LjV0LTMgLTIxLjVsOSAtMzVxMyAtMTIgMTQgLTE5cTcgLTQgMTUgLTRxNCAwIDkgMnE4MCAyNCAxMzguNSA4Mi41dDgyLjUgMTM4LjVxNCAxMyAtMi41IDI0dC0xOC41IDE0bC0zNCA5cS00IDEgLTggMXpNMTAwMCA1MTVxLTIzIDAgLTI5IC0yMnEtMjcgLTk2IC05OCAtMTY2cS03MCAtNzEgLTE2NiAtOThxLTExIC0zIC0xNy41IC0xMy41dC0zLjUgLTIyLjVsOSAtMzVxMyAtMTMgMTQgLTE5cTcgLTQgMTUgLTQgcTQgMCA4IDFxMTIxIDM0IDIwOS41IDEyMi41dDEyMi41IDIwOS41cTQgMTIgLTIuNSAyM3QtMTguNSAxNGwtMzYgOXEtMyAxIC03IDF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIwMjsiIGQ9Ik03MDAgODAwaDMwMHYtMzgwaC0xODB2MjAwaC0zNDB2LTIwMGgtMzgwdjc1NXEwIDEwIDcuNSAxNy41dDE3LjUgNy41aDU3NXYtNDAwek0xMDAwIDkwMGgtMjAwdjIwMHpNNzAwIDMwMGgxNjJsLTIxMiAtMjEybC0yMTIgMjEyaDE2MnYyMDBoMTAwdi0yMDB6TTUyMCAwaC0zOTVxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYzOTV6TTEwMDAgMjIwdi0xOTVxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0xOTV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIwMzsiIGQ9Ik03MDAgODAwaDMwMHYtNTIwbC0zNTAgMzUwbC01NTAgLTU1MHYxMDk1cTAgMTAgNy41IDE3LjV0MTcuNSA3LjVoNTc1di00MDB6TTEwMDAgOTAwaC0yMDB2MjAwek04NjIgMjAwaC0xNjJ2LTIwMGgtMTAwdjIwMGgtMTYybDIxMiAyMTJ6TTQ4MCAwaC0zNTVxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY1NWgzODB2LTgwek0xMDAwIDgwdi01NXEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTE1NXY4MGgxODB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIwNDsiIGQ9Ik0xMTYyIDgwMGgtMTYydi0yMDBoMTAwbDEwMCAtMTAwaC0zMDB2MzAwaC0xNjJsMjEyIDIxMnpNMjAwIDgwMGgyMDBxMjcgMCA0MCAtMnQyOS41IC0xMC41dDIzLjUgLTMwdDcgLTU3LjVoMzAwdi0xMDBoLTYwMGwtMjAwIC0zNTB2NDUwaDEwMHEwIDM2IDcgNTcuNXQyMy41IDMwdDI5LjUgMTAuNXQ0MCAyek04MDAgNDAwaDI0MGwtMjQwIC00MDBoLTgwMGwzMDAgNTAwaDUwMHYtMTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMDU7IiBkPSJNNjUwIDExMDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGg1MHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMTAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djEwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoNTB2NTBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek0xMDAwIDg1MHYxNTBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTgwMCBxMCAtNDEgLTI5LjUgLTcwLjV0LTcwLjUgLTI5LjVoLTYwMHEtMSAwIC0yMCA0bDI0NiAyNDZsLTMyNiAzMjZ2MzI0cTAgNDEgMjkuNSA3MC41dDcwLjUgMjkuNXYtMTUwcTAgLTYyIDQ0IC0xMDZ0MTA2IC00NGgzMDBxNjIgMCAxMDYgNDR0NDQgMTA2ek00MTIgMjUwbC0yMTIgLTIxMnYxNjJoLTIwMHYxMDBoMjAwdjE2MnoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjIxOyIgZD0iTTQ4MCAxMTY1bDY4MiAtNjgzcTMxIC0zMSAzMSAtNzUuNXQtMzEgLTc1LjVsLTEzMSAtMTMxaC00ODFsLTUxNyA1MThxLTMyIDMxIC0zMiA3NS41dDMyIDc1LjVsMjk1IDI5NnEzMSAzMSA3NS41IDMxdDc2LjUgLTMxek0xMDggNzk0bDM0MiAtMzQybDMwMyAzMDRsLTM0MSAzNDF6TTI1MCAxMDBoODAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtOTAwdjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjQxOyIgZD0iTTEyMDAgOTAwaC01MHEwIDIxIC00IDM3dC05LjUgMjYuNXQtMTggMTcuNXQtMjIgMTF0LTI4LjUgNS41dC0zMSAydC0zNyAwLjVoLTIwMHYtODUwcTAgLTIyIDI1IC0zNC41dDUwIC0xMy41bDI1IC0ydi0xMDBoLTQwMHYxMDBxNCAwIDExIDAuNXQyNCAzdDMwIDd0MjQgMTV0MTEgMjQuNXY4NTBoLTIwMHEtMjUgMCAtMzcgLTAuNXQtMzEgLTJ0LTI4LjUgLTUuNXQtMjIgLTExdC0xOCAtMTcuNXQtOS41IC0yNi41dC00IC0zN2gtNTB2MzAwIGgxMDAwdi0zMDB6TTUwMCA0NTBoLTI1cTAgMTUgLTQgMjQuNXQtOSAxNC41dC0xNyA3LjV0LTIwIDN0LTI1IDAuNWgtMTAwdi00MjVxMCAtMTEgMTIuNSAtMTcuNXQyNS41IC03LjVoMTJ2LTUwaC0yMDB2NTBxNTAgMCA1MCAyNXY0MjVoLTEwMHEtMTcgMCAtMjUgLTAuNXQtMjAgLTN0LTE3IC03LjV0LTkgLTE0LjV0LTQgLTI0LjVoLTI1djE1MGg1MDB2LTE1MHoiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUwOyIgZD0iTTg2NSA1NjVsLTQ5NCAtNDk0cS0yMyAtMjMgLTQxIC0yM3EtMTQgMCAtMjIgMTMuNXQtOCAzOC41djEwMDBxMCAyNSA4IDM4LjV0MjIgMTMuNXExOCAwIDQxIC0yM2w0OTQgLTQ5NHExNCAtMTQgMTQgLTM1dC0xNCAtMzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI1MTsiIGQ9Ik0zMzUgNjM1bDQ5NCA0OTRxMjkgMjkgNTAgMjAuNXQyMSAtNDkuNXYtMTAwMHEwIC00MSAtMjEgLTQ5LjV0LTUwIDIwLjVsLTQ5NCA0OTRxLTE0IDE0IC0xNCAzNXQxNCAzNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUyOyIgZD0iTTEwMCA5MDBoMTAwMHE0MSAwIDQ5LjUgLTIxdC0yMC41IC01MGwtNDk0IC00OTRxLTE0IC0xNCAtMzUgLTE0dC0zNSAxNGwtNDk0IDQ5NHEtMjkgMjkgLTIwLjUgNTB0NDkuNSAyMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUzOyIgZD0iTTYzNSA4NjVsNDk0IC00OTRxMjkgLTI5IDIwLjUgLTUwdC00OS41IC0yMWgtMTAwMHEtNDEgMCAtNDkuNSAyMXQyMC41IDUwbDQ5NCA0OTRxMTQgMTQgMzUgMTR0MzUgLTE0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNTQ7IiBkPSJNNzAwIDc0MXYtMTgybC02OTIgLTMyM3YyMjFsNDEzIDE5M2wtNDEzIDE5M3YyMjF6TTEyMDAgMGgtODAwdjIwMGg4MDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU1OyIgZD0iTTEyMDAgOTAwaC0yMDB2LTEwMGgyMDB2LTEwMGgtMzAwdjMwMGgyMDB2MTAwaC0yMDB2MTAwaDMwMHYtMzAwek0wIDcwMGg1MHEwIDIxIDQgMzd0OS41IDI2LjV0MTggMTcuNXQyMiAxMXQyOC41IDUuNXQzMSAydDM3IDAuNWgxMDB2LTU1MHEwIC0yMiAtMjUgLTM0LjV0LTUwIC0xMy41bC0yNSAtMnYtMTAwaDQwMHYxMDBxLTQgMCAtMTEgMC41dC0yNCAzdC0zMCA3dC0yNCAxNXQtMTEgMjQuNXY1NTBoMTAwcTI1IDAgMzcgLTAuNXQzMSAtMiB0MjguNSAtNS41dDIyIC0xMXQxOCAtMTcuNXQ5LjUgLTI2LjV0NCAtMzdoNTB2MzAwaC04MDB2LTMwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU2OyIgZD0iTTgwMCA3MDBoLTUwcTAgMjEgLTQgMzd0LTkuNSAyNi41dC0xOCAxNy41dC0yMiAxMXQtMjguNSA1LjV0LTMxIDJ0LTM3IDAuNWgtMTAwdi01NTBxMCAtMjIgMjUgLTM0LjV0NTAgLTE0LjVsMjUgLTF2LTEwMGgtNDAwdjEwMHE0IDAgMTEgMC41dDI0IDN0MzAgN3QyNCAxNXQxMSAyNC41djU1MGgtMTAwcS0yNSAwIC0zNyAtMC41dC0zMSAtMnQtMjguNSAtNS41dC0yMiAtM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<script src="data:application/x-javascript;base64,/*!
 * Bootstrap v3.3.5 (http://getbootstrap.com)
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<h1 class="title toc-ignore">BJNP_PNAS_replication_SI_Tables.R</h1>
<h4 class="author"><em>mbuntaine</em></h4>
<h4 class="date"><em>Fri Apr 27 10:19:13 2018</em></h4>

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<pre class="r"><code>######################################################################################
######################################################################################
#Replication code for:

#Mark T. Buntaine, Ryan Jablonski, Daniel Nielson, Paula Pickering
#SMS Texts on Corruption Help Ugandan Voters Hold Elected Councillors Accountable at the Polls

#Prepared by: Mark Buntaine
#Mark Buntaine contact (as of April 2018): buntaine@bren.ucsb.edu

#Compiled using R Version 3.4.3 (version &quot;Kite-Eating Tree&quot;) on Mac running OS X 10.13.4
######################################################################################
######################################################################################

options(warn=-1)
#Note: warnings turned off for the replication code log file

######################################################################################
###Packages
######################################################################################
library(lfe) #Version 2.6-2291</code></pre>
<pre><code>## Loading required package: Matrix</code></pre>
<pre class="r"><code>#Note: on warnings, see: https://cran.r-project.org/web/packages/lfe/vignettes/identification.pdf
library(stargazer) #Version 5.2.1</code></pre>
<pre><code>## 
## Please cite as:</code></pre>
<pre><code>##  Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.</code></pre>
<pre><code>##  R package version 5.2.1. https://CRAN.R-project.org/package=stargazer</code></pre>
<pre class="r"><code>library(dplyr) #Version 0.7.4</code></pre>
<pre><code>## 
## Attaching package: 'dplyr'</code></pre>
<pre><code>## The following objects are masked from 'package:stats':
## 
##     filter, lag</code></pre>
<pre><code>## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union</code></pre>
<pre class="r"><code>library(cobalt) #Version 3.2.2
library(xtable) #Version 1.8-2

######################################################################################
###Functions
######################################################################################
felm.ri &lt;- function(formula, dta, treat.var, rand.ob, rand.ob.info.cols, join.var, sims, ...){
  require(lfe)
  print(&quot;1. data and rand.ob must have rows organized in identical order&quot;)
  print(&quot;2. treat.var should be first right-side entry in formula&quot;)
  print(&quot;3. join.var must have identical name between data and rand.ob&quot;)
  
  ate &lt;- coef(felm(formula, data=dta))[1]
  N &lt;- felm(formula, data=dta)$N
  ate.samp.dist &lt;- rep(NA,sims)
  
  for (i in 1:sims){
    dta[,treat.var] &lt;- rand.ob[rand.ob[,join.var] %in% dta[,join.var], rand.ob.info.cols+i]
    
    ate.samp.dist[i] &lt;- coef(felm(formula, data=dta))[1]
  }
  
  p.two.way &lt;- sum(abs(ate)&lt;abs(ate.samp.dist))/sims
  p.one.way.greater &lt;- sum(ate&lt;ate.samp.dist)/sims
  p.one.way.lesser &lt;- sum(ate&gt;ate.samp.dist)/sims
  se &lt;- sd(ate.samp.dist)
  
  coun &lt;- list(&quot;ate&quot; = ate, &quot;ate.samp.dist&quot; = ate.samp.dist, &quot;se&quot;=se, &quot;p.two.way&quot; = p.two.way, &quot;p.one.way.greater&quot; = p.one.way.greater, &quot;p.one.way.lesser&quot; = p.one.way.lesser, &quot;N&quot; = N)
  return(coun)
}

felm.ri2 &lt;- function(formula, dta, treat.var, rand.ob, rand.ob.info.cols, join.var, sims, ...){
  require(lfe)
  print(&quot;1. data and rand.ob must have rows organized in identical order&quot;)
  print(&quot;2. treat.var should be first right-side entry in formula&quot;)
  print(&quot;3. join.var must have identical name between data and rand.ob&quot;)
  
  #Note: have to make this for multiple factor of one randomized treatment
  
  ran.coef.num &lt;- length(unique(dta[,treat.var][!is.na(dta[,treat.var])]))-1 #Gives number of coefficients to keep for crossed treatment indicator
  N &lt;- felm(formula, data=dta)$N
  coef &lt;- coef(felm(formula, data=dta))[1:ran.coef.num]
  coef.samp.dist &lt;- matrix(data=NA, nrow=ran.coef.num, ncol=sims)
  #row.names(coef.samp.dist) &lt;- names(coef)
  
  for (i in 1:sims){
    dta[,treat.var] &lt;- rand.ob[rand.ob[,join.var] %in% dta[,join.var], rand.ob.info.cols+i]
    coef.samp.dist[,i] &lt;- coef(felm(formula, data=dta))[1:ran.coef.num]
  }
  
  se &lt;- apply(coef.samp.dist, 1, sd) #Cannot get SEs off non-randomized parameters
  
  p.two.way &lt;- rep(NA,length(coef))
  p.one.way.greater &lt;- rep(NA,length(coef))
  p.one.way.lesser &lt;- rep(NA,length(coef))
  
  for (i in 1:length(coef)){
    p.two.way[i] &lt;- sum(abs(coef[i])&lt;abs(coef.samp.dist[i,]))/sims
    p.one.way.greater[i] &lt;- sum(coef[i]&lt;coef.samp.dist[i,])/sims
    p.one.way.lesser[i] &lt;- sum(coef[i]&gt;coef.samp.dist[i,])/sims
  }
  
  coun &lt;- list(&quot;coef&quot; = coef, &quot;coef.samp.dist&quot; = coef.samp.dist, &quot;se&quot;=se, &quot;p.two.way&quot; = p.two.way, &quot;p.one.way.greater&quot; = p.one.way.greater, &quot;p.one.way.lesser&quot; = p.one.way.lesser, &quot;N&quot; = N)
  return(coun)
}

felm.ri3 &lt;- function(formula, dta, treat.var, rand.ob, rand.ob.info.cols, join.var, sims, weights, ...){
  require(lfe)
  print(&quot;1. data and rand.ob must have rows organized in identical order&quot;)
  print(&quot;2. treat.var should be first right-side entry in formula&quot;)
  print(&quot;3. join.var must have identical name between data and rand.ob&quot;)
  
  ate &lt;- coef(felm(formula, data=dta, weights = weights))[1]
  N &lt;- felm(formula, data=dta)$N
  ate.samp.dist &lt;- rep(NA,sims)
  
  for (i in 1:sims){
    dta[,treat.var] &lt;- rand.ob[rand.ob[,join.var] %in% dta[,join.var], rand.ob.info.cols+i]
    
    ate.samp.dist[i] &lt;- coef(felm(formula, data=dta))[1]
  }
  
  p.two.way &lt;- sum(abs(ate)&lt;abs(ate.samp.dist))/sims
  p.one.way.greater &lt;- sum(ate&lt;ate.samp.dist)/sims
  p.one.way.lesser &lt;- sum(ate&gt;ate.samp.dist)/sims
  se &lt;- sd(ate.samp.dist)
  
  coun &lt;- list(&quot;ate&quot; = ate, &quot;ate.samp.dist&quot; = ate.samp.dist, &quot;se&quot;=se, &quot;p.two.way&quot; = p.two.way, &quot;p.one.way.greater&quot; = p.one.way.greater, &quot;p.one.way.lesser&quot; = p.one.way.lesser, &quot;N&quot; = N)
  return(coun)
}

######################################################################################
###Data input
######################################################################################

#Update working directory as needed to folder with replication materials
#setwd(&quot;D:/google drive/Uganda Vote Choice project/Analysis/Replication/Replication_PNAS/&quot;)
setwd(&quot;~/Google Drive/Uganda Vote Choice project/Analysis/Replication/Replication_PNAS/&quot;)

data &lt;- read.csv(&quot;./data/MASTER_Analysis_Anon_180424.csv&quot;, stringsAsFactors=FALSE)

Budget_RI &lt;- read.csv(&quot;./data/Budget_RI.csv&quot;, stringsAsFactors=FALSE)
Budget_RI &lt;- Budget_RI[match(data$id.cleaned, Budget_RI$id.cleaned),] #reordering to match &quot;data&quot;

Density_RI &lt;- read.csv(&quot;./data/Density_RI.csv&quot;, stringsAsFactors=FALSE)
Density_RI &lt;- Density_RI[match(data$id.cleaned, Density_RI$id.cleaned),] #reordering to match &quot;data&quot;


######################################################################################
###Data setup
######################################################################################
schooling.levels &lt;- c(&quot;no_schooling&quot;,&quot;some_primary_s&quot;,&quot;completed_prim&quot;,&quot;some_secondary&quot;,&quot;completed_seco&quot;,&quot;some_universit&quot;,&quot;completed_univ&quot;,&quot;some_post_grad&quot;,&quot;completed_mast&quot;,&quot;refuse_to_answ&quot;)
data$r.education &lt;- factor(data$r.How_much_schooling_have_you_co, levels=schooling.levels)

#Remaking &quot;bd.multi.treat2&quot; because of unwanted class conversion in &quot;data&quot; from read.csv()
data$bd.multi.treat2 &lt;- ifelse(data$budget.treat==1 &amp; data$density.treat2==1,&quot;1.1&quot;,NA)
data$bd.multi.treat2 &lt;- ifelse(data$budget.treat==1 &amp; data$density.treat2==0,&quot;1.0&quot;,data$bd.multi.treat2)
data$bd.multi.treat2 &lt;- ifelse(data$budget.treat==0 &amp; data$density.treat2==1,&quot;0.1&quot;,data$bd.multi.treat2)
data$bd.multi.treat2 &lt;- ifelse(data$budget.treat==0 &amp; data$density.treat2==0,&quot;0.0&quot;,data$bd.multi.treat2)

data$partyID.slim &lt;- factor(data$partyID.slim, levels=c(&quot;nrm&quot;,&quot;opposition&quot;,&quot;independent&quot;))

data$aligned.lc5.chair.inc.noNA &lt;- ifelse(is.na(data$aligned.lc5.chair.inc), &quot;missing&quot;, data$aligned.lc5.chair.inc)
data$aligned.lc5.councillor.inc.noNA &lt;- ifelse(is.na(data$aligned.lc5.councillor.inc), &quot;missing&quot;, data$aligned.lc5.councillor.inc)
data$aligned.lc3.chair.inc.noNA &lt;- ifelse(is.na(data$aligned.lc3.chair.inc), &quot;missing&quot;, data$aligned.lc3.chair.inc)
data$aligned.lc3.councillor.inc.noNA &lt;- ifelse(is.na(data$aligned.lc3.councillor.inc), &quot;missing&quot;, data$aligned.lc3.councillor.inc)

data$lc5.chair.inc.vote.orig &lt;- ifelse(data$lc5.chair.party.vote==data$lc5.chair.party.orig, 1, 0)
data$lc5.councillor.inc.vote.orig &lt;- ifelse(data$lc5.councillor.party.vote==data$lc5.councillor.party.orig, 1, 0)
#Note: &quot;orig&quot; variable is the party elected to the seat in 2011, regardless of subsequent party switching

#Fixed effect weights
data$fe.w &lt;- ifelse(is.na(data$density.treat2), 1/(.5*.5), 1/(.8*.2))


######################################################################################
###Budget subsets
######################################################################################

#Prior-defined subgroups
budget.good &lt;- subset(data, budget.actual &gt; budget.prior | (budget.actual==budget.prior &amp; budget.actual&gt;=4))
budget.bad &lt;- subset(data, budget.actual &lt; budget.prior | (budget.actual==budget.prior &amp; budget.actual&lt;=2))

#These are the subgroups not defined by priors, for an extended analysis
budget.positive &lt;- subset(data, budget.actual&gt;=4)
budget.negative &lt;- subset(data, budget.actual&lt;=2)

#Main subsets: no uncontested elections, no individual incumbent switched parties and ran again in 2016, and no redistricting (includes elections w/o incumbent individual)
budget.good.lc5.chair.comp &lt;- subset(budget.good, lc5.chair.competitive==1 &amp; lc5.chair.party.switch==0)
budget.good.lc5.councillor.comp &lt;- subset(budget.good, lc5.councillor.competitive==1 &amp; lc5.councillor.party.switch==0 &amp; is.na(lc5.councillor.redistricted2016))
budget.bad.lc5.chair.comp &lt;- subset(budget.bad, lc5.chair.competitive==1 &amp; lc5.chair.party.switch==0)
budget.bad.lc5.councillor.comp &lt;- subset(budget.bad, lc5.councillor.competitive==1 &amp; lc5.councillor.party.switch==0 &amp; is.na(lc5.councillor.redistricted2016))
budget.positive.lc5.chair.comp &lt;- subset(budget.positive, lc5.chair.competitive==1 &amp; lc5.chair.party.switch==0)
budget.positive.lc5.councillor.comp &lt;- subset(budget.positive, lc5.councillor.competitive==1 &amp; lc5.councillor.party.switch==0 &amp; is.na(lc5.councillor.redistricted2016))
budget.negative.lc5.chair.comp &lt;- subset(budget.negative, lc5.chair.competitive==1 &amp; lc5.chair.party.switch==0)
budget.negative.lc5.councillor.comp &lt;- subset(budget.negative, lc5.councillor.competitive==1 &amp; lc5.councillor.party.switch==0 &amp; is.na(lc5.councillor.redistricted2016))

#Verified Recipient subgroups
budget.good.lc5.chair.comp.c &lt;- subset(budget.good, lc5.chair.competitive==1 &amp; lc5.chair.party.switch==0 &amp; d.X_11_Over_the_last_several_days==&quot;yes&quot;)
budget.good.lc5.councillor.comp.c &lt;- subset(budget.good, lc5.councillor.competitive==1 &amp; lc5.councillor.party.switch==0 &amp; is.na(lc5.councillor.redistricted2016) &amp; d.X_11_Over_the_last_several_days==&quot;yes&quot;)
budget.bad.lc5.chair.comp.c &lt;- subset(budget.bad, lc5.chair.competitive==1 &amp; lc5.chair.party.switch==0 &amp; d.X_11_Over_the_last_several_days==&quot;yes&quot;)
budget.bad.lc5.councillor.comp.c &lt;- subset(budget.bad, lc5.councillor.competitive==1 &amp; lc5.councillor.party.switch==0 &amp; is.na(lc5.councillor.redistricted2016) &amp; d.X_11_Over_the_last_several_days==&quot;yes&quot;)


######################################################################################
###Table S2: Conditional effects of budget treatment by trust in Twaweza and Auditor General
######################################################################################

#Models
h1a.b.1.ag &lt;- felm(lc5.chair.inc.vote ~ budget.treat*b.trust.AG + lc5.chair.intent + b.Q1_living_conditions + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
h1a.b.2.ag &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*b.trust.AG + lc5.councillor.intent + b.Q1_living_conditions + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
h1b.b.1.ag &lt;- felm(lc5.chair.inc.vote ~ budget.treat*b.trust.AG + lc5.chair.intent + b.Q1_living_conditions + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
h1b.b.2.ag &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*b.trust.AG + lc5.councillor.intent + b.Q1_living_conditions + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)
h1a.b.1.tw &lt;- felm(lc5.chair.inc.vote ~ budget.treat*b.trust.Twaweza + lc5.chair.intent + b.Q1_living_conditions + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
h1a.b.2.tw &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*b.trust.Twaweza + lc5.councillor.intent + b.Q1_living_conditions + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
h1b.b.1.tw &lt;- felm(lc5.chair.inc.vote ~ budget.treat*b.trust.Twaweza + lc5.chair.intent + b.Q1_living_conditions + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
h1b.b.2.tw &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*b.trust.Twaweza + lc5.councillor.intent + b.Q1_living_conditions + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)

stargazer(h1a.b.1.ag, h1a.b.2.ag, h1b.b.1.ag, h1b.b.2.ag, h1a.b.1.tw, h1a.b.2.tw, h1b.b.1.tw, h1b.b.2.tw, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;,&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Trust in Auditor General&quot;,&quot;Trust in Twaweza&quot;), column.separate = c(4,4),
          keep = c(&quot;budget.treat&quot;,&quot;b.trust.AG&quot;,&quot;b.trust.Twaweza&quot;),
          covariate.labels = c(&quot;Budget Treatment&quot;,&quot;AG Trust: Don't Know&quot;,&quot;AG Trust: Missing&quot;,&quot;AG Trust: A Little&quot;,&quot;AG Trust: A Lot&quot;,&quot;Twaweza Trust: Don't Know&quot;,&quot;Twaweza Trust: Missing&quot;,&quot;Twaweza Trust: A Little&quot;,&quot;Twaweza Trust: A Lot&quot;, &quot;Treatment * AG Trust: Don't Know&quot;,&quot;Treatment * AG Trust: Missing&quot;,&quot;Treatment * AG Trust: A Little&quot;,&quot;Treatment * AG Trust: A Lot&quot;, &quot;Treatment * Twaweza Trust: Don't Know&quot;,&quot;Treatment * Twaweza Trust: Missing&quot;,&quot;Treatment * Twaweza Trust: A Little&quot;,&quot;Treatment * Twaweza Trust: A Lot&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;News Type&quot;,&quot;Good&quot;,&quot;Good&quot;,&quot;Bad&quot;,&quot;Bad&quot;,&quot;Good&quot;,&quot;Good&quot;,&quot;Bad&quot;,&quot;Bad&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          notes = &quot;Notes: SEs clustered by politician; two-tailed tests; contested elections only without party switching by incumbent.&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Date and time: Wed, Dec 06, 2017 - 11:28:41</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 10:22:10
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{8}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-9} 
## \\[-1.8ex] &amp; LC5 Chair &amp; LC5 Councillor &amp; LC5 Chair &amp; LC5 Councillor &amp; LC5 Chair &amp; LC5 Councillor &amp; LC5 Chair &amp; LC5 Councillor \\ 
##  &amp; \multicolumn{4}{c}{Trust in Auditor General} &amp; \multicolumn{4}{c}{Trust in Twaweza} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4) &amp; (5) &amp; (6) &amp; (7) &amp; (8)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment &amp; $-$0.047 &amp; $-$0.047 &amp; 0.003 &amp; $-$0.040 &amp; 0.093 &amp; 0.183 &amp; $-$0.026 &amp; 0.064 \\ 
##   &amp; (0.022) &amp; (0.039) &amp; (0.038) &amp; (0.034) &amp; (0.085) &amp; (0.085) &amp; (0.079) &amp; (0.061) \\ 
##   &amp; p = 0.036 &amp; p = 0.224 &amp; p = 0.936 &amp; p = 0.245 &amp; p = 0.275 &amp; p = 0.031 &amp; p = 0.741 &amp; p = 0.297 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  AG Trust: Don't Know &amp; $-$0.011 &amp; $-$0.052 &amp; $-$0.014 &amp; $-$0.034 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; (0.015) &amp; (0.038) &amp; (0.017) &amp; (0.039) &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; p = 0.467 &amp; p = 0.176 &amp; p = 0.401 &amp; p = 0.383 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  AG Trust: Missing &amp; $-$0.098 &amp; 0.074 &amp; 0.030 &amp; $-$0.043 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; (0.099) &amp; (0.142) &amp; (0.059) &amp; (0.037) &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; p = 0.322 &amp; p = 0.602 &amp; p = 0.606 &amp; p = 0.248 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  AG Trust: A Little &amp; $-$0.026 &amp; $-$0.049 &amp; $-$0.002 &amp; 0.011 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; (0.027) &amp; (0.038) &amp; (0.030) &amp; (0.037) &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; p = 0.332 &amp; p = 0.197 &amp; p = 0.937 &amp; p = 0.777 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  AG Trust: A Lot &amp; 0.002 &amp; 0.008 &amp; 0.026 &amp; 0.014 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; (0.022) &amp; (0.050) &amp; (0.044) &amp; (0.038) &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; p = 0.922 &amp; p = 0.878 &amp; p = 0.548 &amp; p = 0.706 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Twaweza Trust: Don't Know &amp;  &amp;  &amp;  &amp;  &amp; 0.063 &amp; 0.045 &amp; 0.083 &amp; 0.043 \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; (0.091) &amp; (0.076) &amp; (0.072) &amp; (0.064) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; p = 0.493 &amp; p = 0.559 &amp; p = 0.249 &amp; p = 0.502 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Twaweza Trust: Missing &amp;  &amp;  &amp;  &amp;  &amp; $-$0.335 &amp; 0.069 &amp; 0.022 &amp; $-$0.002 \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; (0.152) &amp; (0.166) &amp; (0.202) &amp; (0.056) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; p = 0.028 &amp; p = 0.677 &amp; p = 0.913 &amp; p = 0.968 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Twaweza Trust: A Little &amp;  &amp;  &amp;  &amp;  &amp; 0.026 &amp; 0.103 &amp; 0.059 &amp; 0.022 \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; (0.082) &amp; (0.075) &amp; (0.064) &amp; (0.048) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; p = 0.751 &amp; p = 0.169 &amp; p = 0.356 &amp; p = 0.656 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Twaweza Trust: A Lot &amp;  &amp;  &amp;  &amp;  &amp; 0.045 &amp; 0.077 &amp; 0.063 &amp; 0.031 \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; (0.084) &amp; (0.074) &amp; (0.051) &amp; (0.058) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; p = 0.591 &amp; p = 0.299 &amp; p = 0.223 &amp; p = 0.601 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Treatment * AG Trust: Don't Know &amp; 0.054 &amp; 0.094 &amp; 0.008 &amp; 0.026 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; (0.020) &amp; (0.059) &amp; (0.029) &amp; (0.045) &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; p = 0.007 &amp; p = 0.108 &amp; p = 0.796 &amp; p = 0.561 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Treatment * AG Trust: Missing &amp; 0.154 &amp; 0.036 &amp; $-$0.109 &amp; 0.029 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; (0.170) &amp; (0.223) &amp; (0.082) &amp; (0.070) &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; p = 0.364 &amp; p = 0.873 &amp; p = 0.183 &amp; p = 0.679 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Treatment * AG Trust: A Little &amp; 0.064 &amp; 0.102 &amp; 0.027 &amp; 0.002 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; (0.037) &amp; (0.052) &amp; (0.046) &amp; (0.042) &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; p = 0.084 &amp; p = 0.050 &amp; p = 0.553 &amp; p = 0.963 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Treatment * AG Trust: A Lot &amp; 0.076 &amp; 0.092 &amp; $-$0.029 &amp; $-$0.022 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; (0.032) &amp; (0.065) &amp; (0.069) &amp; (0.044) &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; p = 0.019 &amp; p = 0.159 &amp; p = 0.669 &amp; p = 0.610 &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Treatment * Twaweza Trust: Don't Know &amp;  &amp;  &amp;  &amp;  &amp; $-$0.154 &amp; $-$0.137 &amp; 0.015 &amp; $-$0.114 \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; (0.087) &amp; (0.097) &amp; (0.096) &amp; (0.075) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; p = 0.078 &amp; p = 0.156 &amp; p = 0.877 &amp; p = 0.129 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Treatment * Twaweza Trust: Missing &amp;  &amp;  &amp;  &amp;  &amp; 0.614 &amp; $-$0.203 &amp; $-$0.039 &amp; $-$0.027 \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; (0.164) &amp; (0.218) &amp; (0.191) &amp; (0.117) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; p = 0.0002 &amp; p = 0.353 &amp; p = 0.840 &amp; p = 0.814 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Treatment * Twaweza Trust: A Little &amp;  &amp;  &amp;  &amp;  &amp; $-$0.084 &amp; $-$0.173 &amp; 0.051 &amp; $-$0.080 \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; (0.093) &amp; (0.093) &amp; (0.080) &amp; (0.063) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; p = 0.370 &amp; p = 0.063 &amp; p = 0.520 &amp; p = 0.206 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Treatment * Twaweza Trust: A Lot &amp;  &amp;  &amp;  &amp;  &amp; $-$0.086 &amp; $-$0.151 &amp; 0.028 &amp; $-$0.125 \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; (0.096) &amp; (0.088) &amp; (0.081) &amp; (0.070) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp; p = 0.373 &amp; p = 0.088 &amp; p = 0.728 &amp; p = 0.073 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## News Type &amp; Good &amp; Good &amp; Bad &amp; Bad &amp; Good &amp; Good &amp; Bad &amp; Bad \\ 
## Observations &amp; 3,921 &amp; 3,074 &amp; 3,131 &amp; 2,883 &amp; 3,921 &amp; 3,074 &amp; 3,131 &amp; 2,883 \\ 
## Adjusted R$^{2}$ &amp; 0.205 &amp; 0.325 &amp; 0.344 &amp; 0.396 &amp; 0.206 &amp; 0.323 &amp; 0.344 &amp; 0.396 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{9}{r}{Notes: SEs clustered by politician; two-tailed tests; contested elections only without party switching by incumbent.} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S3: Effect of treatment on reported vote choice among voters who expect free and fair district elections
######################################################################################

budget.good.lc5.chair.comp.ff &lt;- subset(budget.good.lc5.chair.comp, (b.Q24_Powerful_ppl_learning_how_==&quot;not_at_all_lik&quot; | b.Q24_Powerful_ppl_learning_how_==&quot;not_very_likel&quot;) &amp; (b.Q25_Will_counting_votes_be_fai==&quot;very_likely&quot; | b.Q25_Will_counting_votes_be_fai==&quot;somewhat_likel&quot;))
budget.good.lc5.councillor.comp.ff &lt;- subset(budget.good.lc5.councillor.comp, (b.Q24_Powerful_ppl_learning_how_==&quot;not_at_all_lik&quot; | b.Q24_Powerful_ppl_learning_how_==&quot;not_very_likel&quot;) &amp; (b.Q25_Will_counting_votes_be_fai==&quot;very_likely&quot; | b.Q25_Will_counting_votes_be_fai==&quot;somewhat_likel&quot;))
budget.bad.lc5.chair.comp.ff &lt;- subset(budget.bad.lc5.chair.comp, (b.Q24_Powerful_ppl_learning_how_==&quot;not_at_all_lik&quot; | b.Q24_Powerful_ppl_learning_how_==&quot;not_very_likel&quot;) &amp; (b.Q25_Will_counting_votes_be_fai==&quot;very_likely&quot; | b.Q25_Will_counting_votes_be_fai==&quot;somewhat_likel&quot;))
budget.bad.lc5.councillor.comp.ff &lt;- subset(budget.bad.lc5.councillor.comp, (b.Q24_Powerful_ppl_learning_how_==&quot;not_at_all_lik&quot; | b.Q24_Powerful_ppl_learning_how_==&quot;not_very_likel&quot;) &amp; (b.Q25_Will_counting_votes_be_fai==&quot;very_likely&quot; | b.Q25_Will_counting_votes_be_fai==&quot;somewhat_likel&quot;))

h1a.b.1ff &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp.ff)
h1a.b.2ff &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp.ff)
h1b.b.1ff &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp.ff)
h1b.b.2ff &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp.ff)

chair.good.ff &lt;- felm.ri(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name,
                         dta=budget.good.lc5.chair.comp.ff, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                         rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.good.ff &lt;- felm.ri(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name,
                        dta=budget.good.lc5.councillor.comp.ff, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                        rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.bad.ff &lt;- felm.ri(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, 
                        dta=budget.bad.lc5.chair.comp.ff, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                        rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.bad.ff &lt;- felm.ri(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, 
                       dta=budget.bad.lc5.councillor.comp.ff, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                       rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>stargazer(h1a.b.1ff, h1a.b.2ff, h1b.b.1ff, h1b.b.2ff, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(2,2),
          keep = c(&quot;budget.treat&quot;,&quot;lc5.chair.intent&quot;,&quot;lc5.councillor.intent&quot;),
          add.lines = list(c(&quot;Polling station fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          covariate.labels = c(&quot;Budget Treatment&quot;,&quot;LC5 Chair Intent&quot;,&quot;LC5 Councillor Intent&quot;),
          notes = &quot;Notes: SEs on treatment derived from randomization inference using exact clustered design; SEs on covariates clustered by politician; one-tailed tests as pre-registered; contested elections only with incumbent who did not switch party.&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          p = list(chair.good.ff$p.one.way.greater,
                   coun.good.ff$p.one.way.greater,
                   chair.bad.ff$p.one.way.lesser,
                   coun.bad.ff$p.one.way.lesser),
          se = list(c(chair.good.ff$se,as.numeric(h1a.b.1ff$cse[2])),
                    c(coun.good.ff$se,as.numeric(h1a.b.2ff$cse[2])),
                    c(chair.bad.ff$se,as.numeric(h1b.b.1ff$cse[2])),
                    c(coun.bad.ff$se,as.numeric(h1b.b.2ff$cse[2]))),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Date and time: Wed, Dec 06, 2017 - 13:27:00</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 11:19:01
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{4}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-5} 
## \\[-1.8ex] &amp; LC5 Chair &amp; LC5 Councillor &amp; LC5 Chair &amp; LC5 Councillor \\ 
##  &amp; \multicolumn{2}{c}{Good News} &amp; \multicolumn{2}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment &amp; $-$0.015 &amp; 0.043 &amp; 0.041 &amp; $-$0.039 \\ 
##   &amp; (0.028) &amp; (0.027) &amp; (0.025) &amp; (0.026) \\ 
##   &amp; p = 0.715 &amp; p = 0.069 &amp; p = 0.947 &amp; p = 0.071 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  LC5 Chair Intent &amp; 0.089 &amp;  &amp; 0.032 &amp;  \\ 
##   &amp; (0.023) &amp;  &amp; (0.032) &amp;  \\ 
##   &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  LC5 Councillor Intent &amp;  &amp; 0.063 &amp;  &amp; 0.010 \\ 
##   &amp;  &amp; (0.026) &amp;  &amp; (0.029) \\ 
##   &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Polling station fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Observations &amp; 1,655 &amp; 1,287 &amp; 1,412 &amp; 1,246 \\ 
## Adjusted R$^{2}$ &amp; 0.226 &amp; 0.400 &amp; 0.356 &amp; 0.396 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{5}{r}{Notes: SEs on treatment derived from randomization inference using exact clustered design; SEs on covariates clustered by politician; one-tailed tests as pre-registered; contested elections only with incumbent who did not switch party.} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S4: Conditional effects of budget treatment by unsure budget prior
######################################################################################

h1a.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat*budget.prior.dontknow + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
p1 &lt;- ifelse(h1a.b.1$coef[1]&gt;0, summary(h1a.b.1)$coef[1,4]/2, 1-summary(h1a.b.1)$coef[1,4]/2)

h1a.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*budget.prior.dontknow + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
p2 &lt;- ifelse(h1a.b.2$coef[1]&gt;0, summary(h1a.b.2)$coef[1,4]/2, 1-summary(h1a.b.2)$coef[1,4]/2)

h1b.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat*budget.prior.dontknow + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
p3 &lt;- ifelse(h1b.b.1$coef[1]&lt;0, summary(h1b.b.1)$coef[1,4]/2, 1-summary(h1b.b.1)$coef[1,4]/2)

h1b.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*budget.prior.dontknow + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)
p4 &lt;- ifelse(h1b.b.2$coef[1]&lt;0, summary(h1b.b.2)$coef[1,4]/2, 1-summary(h1b.b.2)$coef[1,4]/2)

h1a.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat*budget.prior.dontknow + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
p5 &lt;- ifelse(h1a.b.1c$coef[1]&gt;0, summary(h1a.b.1c)$coef[1,4]/2, 1-summary(h1a.b.1c)$coef[1,4]/2)

h1a.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*budget.prior.dontknow + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
p6 &lt;- ifelse(h1a.b.2c$coef[1]&gt;0, summary(h1a.b.2c)$coef[1,4]/2, 1-summary(h1a.b.2c)$coef[1,4]/2)

h1b.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat*budget.prior.dontknow + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
p7 &lt;- ifelse(h1b.b.1c$coef[1]&lt;0, summary(h1b.b.1c)$coef[1,4]/2, 1-summary(h1b.b.1c)$coef[1,4]/2)

h1b.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*budget.prior.dontknow + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)
p8 &lt;- ifelse(h1b.b.2c$coef[1]&lt;0, summary(h1b.b.2c)$coef[1,4]/2, 1-summary(h1b.b.2c)$coef[1,4]/2)

#Table for eight models with &amp; without covariates
stargazer(h1a.b.1, h1a.b.1c, h1a.b.2, h1a.b.2c, h1b.b.1, h1b.b.1c, h1b.b.2, h1b.b.2c, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(4,4),
          keep = c(&quot;budget.treat&quot;,&quot;budget.prior.dontknow&quot;,&quot;lc5.chair.intent&quot;,&quot;lc5.councillor.intent&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          covariate.labels = c(&quot;Budget Treatment&quot;,&quot;Unsure Budget Prior&quot;,&quot;LC5 Chair Intent&quot;,&quot;LC5 Councillor Intent&quot;,&quot;Budget Treatment * Unsure Budget Prior&quot;),
          notes = &quot;Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Date and time: Sat, Dec 09, 2017 - 14:55:02</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 11:19:02
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{8}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-9} 
## \\[-1.8ex] &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} \\ 
##  &amp; \multicolumn{4}{c}{Good News} &amp; \multicolumn{4}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4) &amp; (5) &amp; (6) &amp; (7) &amp; (8)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment &amp; 0.003 &amp; 0.003 &amp; 0.029 &amp; 0.029 &amp; 0.014 &amp; 0.013 &amp; $-$0.043 &amp; $-$0.043 \\ 
##   &amp; (0.022) &amp; (0.020) &amp; (0.018) &amp; (0.018) &amp; (0.018) &amp; (0.018) &amp; (0.015) &amp; (0.015) \\ 
##   &amp; p = 0.905 &amp; p = 0.895 &amp; p = 0.098 &amp; p = 0.103 &amp; p = 0.432 &amp; p = 0.480 &amp; p = 0.004 &amp; p = 0.006 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Unsure Budget Prior &amp; 0.067 &amp; 0.055 &amp; $-$0.020 &amp; $-$0.039 &amp; 0.026 &amp; 0.022 &amp; $-$0.006 &amp; $-$0.013 \\ 
##   &amp; (0.022) &amp; (0.028) &amp; (0.033) &amp; (0.033) &amp; (0.023) &amp; (0.023) &amp; (0.021) &amp; (0.021) \\ 
##   &amp; p = 0.003 &amp; p = 0.048 &amp; p = 0.547 &amp; p = 0.250 &amp; p = 0.251 &amp; p = 0.320 &amp; p = 0.762 &amp; p = 0.548 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Chair Intent &amp; 0.083 &amp; 0.076 &amp;  &amp;  &amp; 0.036 &amp; 0.032 &amp;  &amp;  \\ 
##   &amp; (0.038) &amp; (0.033) &amp;  &amp;  &amp; (0.024) &amp; (0.023) &amp;  &amp;  \\ 
##   &amp; p = 0.028 &amp; p = 0.022 &amp;  &amp;  &amp; p = 0.128 &amp; p = 0.169 &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Councillor Intent &amp;  &amp;  &amp; 0.060 &amp; 0.051 &amp;  &amp;  &amp; 0.002 &amp; 0.005 \\ 
##   &amp;  &amp;  &amp; (0.016) &amp; (0.016) &amp;  &amp;  &amp; (0.017) &amp; (0.016) \\ 
##   &amp;  &amp;  &amp; p = 0.0002 &amp; p = 0.002 &amp;  &amp;  &amp; p = 0.914 &amp; p = 0.761 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Budget Treatment * Unsure Budget Prior &amp; $-$0.007 &amp; $-$0.006 &amp; $-$0.011 &amp; $-$0.007 &amp; $-$0.015 &amp; $-$0.015 &amp; 0.046 &amp; 0.039 \\ 
##   &amp; (0.035) &amp; (0.032) &amp; (0.046) &amp; (0.047) &amp; (0.023) &amp; (0.022) &amp; (0.031) &amp; (0.032) \\ 
##   &amp; p = 0.855 &amp; p = 0.861 &amp; p = 0.811 &amp; p = 0.882 &amp; p = 0.513 &amp; p = 0.489 &amp; p = 0.137 &amp; p = 0.234 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes \\ 
## Observations &amp; 3,921 &amp; 3,921 &amp; 3,074 &amp; 3,074 &amp; 3,131 &amp; 3,131 &amp; 2,883 &amp; 2,883 \\ 
## Adjusted R$^{2}$ &amp; 0.201 &amp; 0.206 &amp; 0.317 &amp; 0.324 &amp; 0.341 &amp; 0.344 &amp; 0.392 &amp; 0.396 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{9}{r}{Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S5: Treatment effects of information type unconditional on respondents' priors
######################################################################################

h1a.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=budget.positive.lc5.chair.comp)
p1 &lt;- ifelse(h1a.b.1$coef[1]&gt;0, summary(h1a.b.1)$coef[1,4]/2, 1-summary(h1a.b.1)$coef[1,4]/2)

h1a.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=budget.positive.lc5.councillor.comp)
p2 &lt;- ifelse(h1a.b.2$coef[1]&gt;0, summary(h1a.b.2)$coef[1,4]/2, 1-summary(h1a.b.2)$coef[1,4]/2)

h1b.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=budget.negative.lc5.chair.comp)
p3 &lt;- ifelse(h1b.b.1$coef[1]&lt;0, summary(h1b.b.1)$coef[1,4]/2, 1-summary(h1b.b.1)$coef[1,4]/2)

h1b.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=budget.negative.lc5.councillor.comp)
p4 &lt;- ifelse(h1b.b.2$coef[1]&lt;0, summary(h1b.b.2)$coef[1,4]/2, 1-summary(h1b.b.2)$coef[1,4]/2)

h1a.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.positive.lc5.chair.comp)
p5 &lt;- ifelse(h1a.b.1c$coef[1]&gt;0, summary(h1a.b.1c)$coef[1,4]/2, 1-summary(h1a.b.1c)$coef[1,4]/2)

h1a.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.positive.lc5.councillor.comp)
p6 &lt;- ifelse(h1a.b.2c$coef[1]&gt;0, summary(h1a.b.2c)$coef[1,4]/2, 1-summary(h1a.b.2c)$coef[1,4]/2)

h1b.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.negative.lc5.chair.comp)
p7 &lt;- ifelse(h1b.b.1c$coef[1]&lt;0, summary(h1b.b.1c)$coef[1,4]/2, 1-summary(h1b.b.1c)$coef[1,4]/2)

h1b.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.negative.lc5.councillor.comp)
p8 &lt;- ifelse(h1b.b.2c$coef[1]&lt;0, summary(h1b.b.2c)$coef[1,4]/2, 1-summary(h1b.b.2c)$coef[1,4]/2)

#Table for eight models with &amp; without covariates
stargazer(h1a.b.1, h1a.b.1c, h1a.b.2, h1a.b.2c, h1b.b.1, h1b.b.1c, h1b.b.2, h1b.b.2c, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(4,4),
          keep = c(&quot;budget.treat&quot;,&quot;lc5.chair.intent&quot;,&quot;lc5.councillor.intent&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          covariate.labels = c(&quot;Budget Treatment&quot;,&quot;LC5 Chair Intent&quot;,&quot;LC5 Councillor Intent&quot;),
          notes = &quot;Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          p = list(p1,p5,p2,p6,p3,p7,p4,p8),
          se = list(h1a.b.1$cse[1:2],
                    h1a.b.1c$cse[1:2],
                    h1a.b.2$cse[1:2],
                    h1a.b.2c$cse[1:2],
                    h1b.b.1$cse[1:2],
                    h1b.b.1c$cse[1:2],
                    h1b.b.2$cse[1:2],
                    h1b.b.2c$cse[1:2]),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Mon, Dec 11, 2017 - 14:08:12</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 11:19:03
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{8}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-9} 
## \\[-1.8ex] &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} \\ 
##  &amp; \multicolumn{4}{c}{Good News} &amp; \multicolumn{4}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4) &amp; (5) &amp; (6) &amp; (7) &amp; (8)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment &amp; 0.004 &amp; 0.003 &amp; 0.019 &amp; 0.019 &amp; 0.005 &amp; 0.005 &amp; $-$0.033 &amp; $-$0.034 \\ 
##   &amp; (0.018) &amp; (0.018) &amp; (0.016) &amp; (0.017) &amp; (0.014) &amp; (0.014) &amp; (0.014) &amp; (0.014) \\ 
##   &amp; p = 0.414 &amp; p = 0.437 &amp; p = 0.129 &amp; p = 0.130 &amp; p = 0.650 &amp; p = 0.643 &amp; p = 0.008 &amp; p = 0.007 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Chair Intent &amp; 0.084 &amp; 0.075 &amp;  &amp;  &amp; 0.035 &amp; 0.031 &amp;  &amp;  \\ 
##   &amp; (0.041) &amp; (0.035) &amp;  &amp;  &amp; (0.021) &amp; (0.022) &amp;  &amp;  \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Councillor Intent &amp;  &amp;  &amp; 0.060 &amp; 0.049 &amp;  &amp;  &amp; 0.003 &amp; 0.005 \\ 
##   &amp;  &amp;  &amp; (0.016) &amp; (0.016) &amp;  &amp;  &amp; (0.016) &amp; (0.016) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes \\ 
## Observations &amp; 3,741 &amp; 3,741 &amp; 2,958 &amp; 2,958 &amp; 3,369 &amp; 3,369 &amp; 3,051 &amp; 3,051 \\ 
## Adjusted R$^{2}$ &amp; 0.180 &amp; 0.190 &amp; 0.316 &amp; 0.322 &amp; 0.367 &amp; 0.368 &amp; 0.387 &amp; 0.390 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{9}{r}{Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S6: Balance on all pre-treatment covariates by budget treatment status (matching Fig. S10)
######################################################################################

###Note: the cleaning step is an exact copy of the replication code for SI Fig. S10
###Cleaning the data and levels
covs0 &lt;- data[,c(&quot;budget.treat&quot;,
                 &quot;b.Q1_living_conditions&quot;,
                 &quot;lc5.councillor.same.tribe&quot;,
                 &quot;lc5.turnout.intent&quot;,
                 &quot;lc5.councillor.intent&quot;,
                 &quot;lc5.chair.intent&quot;,
                 &quot;partyID&quot;,
                 &quot;party.attachment&quot;,
                 &quot;b.Q11_2011_Vote_for_LC5councilor&quot;,
                 &quot;b.Q12_2011_vote_for_LC5chair&quot;,
                 &quot;b.vote.buying&quot;,
                 &quot;budget.importance&quot;,
                 &quot;ps.importance&quot;,
                 &quot;b.Q18_Most_important_public_serv&quot;,
                 &quot;b.Q19_Second_most_important_publ&quot;,
                 &quot;b.Q21a_Trust_in_politicians_for_&quot;,
                 &quot;b.trust.Twaweza&quot;,
                 &quot;b.Q21c_Trust_in_NGOs_for_info&quot;,
                 &quot;b.trust.AG&quot;,
                 &quot;b.Q22_LC5_record_of_budget_mgmt&quot;,
                 &quot;budget.certainty&quot;,
                 &quot;b.Q24_Powerful_ppl_learning_how_&quot;,
                 &quot;b.Q25_Will_counting_votes_be_fai&quot;,
                 &quot;r.What_is_your_gender&quot;,
                 &quot;r.education&quot;,
                 &quot;r.In_which_language_would_you_pr&quot;,
                 &quot;r.What_is_your_age&quot;,
                 &quot;d.attr&quot;)
              ]

##Changing variable names for plot labeling
#names(covs0)
names(covs0) &lt;- c(&quot;budget.treat&quot;,&quot;Living Conditions&quot;,&quot;Same Tribe Councillor&quot;,&quot;Turnout Intent&quot;,&quot;Councillor Vote Intent&quot;,
               &quot;Chair Vote Intent&quot;,&quot;Party&quot;,&quot;Party Attachment&quot;,&quot;Voted for Councillor 2011&quot;,&quot;Voted for Chair 2011&quot;,&quot;Vote Buying&quot;,
               &quot;Budget Importance&quot;,&quot;Services Importance&quot;,&quot;Most Important Service&quot;,&quot;Second Important Service&quot;,&quot;Trust Local Politicians&quot;,
               &quot;Trust Twaweza&quot;,&quot;Trust NGOs&quot;,&quot;Trust Auditor General&quot;,&quot;Budget Prior&quot;,&quot;Budget Prior Certainty&quot;,&quot;Voting Secret&quot;,&quot;Vote Count Fair&quot;,
               &quot;Gender&quot;,&quot;Education&quot;,&quot;Language&quot;,&quot;Age&quot;)

##Changing labels within variables
cols &lt;- 2:26
covs0[,cols] = apply(covs0[,cols], 2, function(x) (as.character(x)))

table(covs0[,2], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##         better     don_t_know        missing    much_better     much_worse 
##           4993            182             24            422           1127 
## refused_to_ans       the_same          worse           &lt;NA&gt; 
##             35           3983           5317              0</code></pre>
<pre class="r"><code>covs0[covs0==&quot;don_t_know&quot;] &lt;- &quot;don't know&quot;
covs0[covs0==&quot;much_better&quot;] &lt;- &quot;much better&quot;
covs0[covs0==&quot;much_worse&quot;] &lt;- &quot;much worse&quot;
covs0[covs0==&quot;refused_to_ans&quot;] &lt;- &quot;refused&quot;
covs0[covs0==&quot;the_same&quot;] &lt;- &quot;same&quot;
table(covs0[,3], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##     0     1  &lt;NA&gt; 
##  4592 11467    24</code></pre>
<pre class="r"><code>covs0[,3] &lt;- ifelse(is.na(covs0[,3]),&quot;missing&quot;,covs0[,3])
covs0[,3] &lt;- ifelse(covs0[,3]==&quot; 1&quot;,&quot;yes&quot;,covs0[,3])
covs0[,3] &lt;- ifelse(covs0[,3]==&quot; 0&quot;,&quot;no&quot;,covs0[,3])
table(covs0[,4], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##     0     1  &lt;NA&gt; 
##   929 15099    55</code></pre>
<pre class="r"><code>covs0[,4] &lt;- ifelse(covs0[,4]==&quot; 1&quot;,&quot;yes&quot;,covs0[,4])
covs0[,4] &lt;- ifelse(covs0[,4]==&quot; 0&quot;,&quot;no&quot;,covs0[,4])
covs0[,4] &lt;- ifelse(is.na(covs0[,4]),&quot;missing&quot;,covs0[,4])
table(covs0[,5], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##    0    1 &lt;NA&gt; 
## 6725 8990  368</code></pre>
<pre class="r"><code>covs0[,5] &lt;- ifelse(covs0[,5]==&quot; 1&quot;,&quot;yes&quot;,covs0[,5])
covs0[,5] &lt;- ifelse(covs0[,5]==&quot; 0&quot;,&quot;no&quot;,covs0[,5])
covs0[,5] &lt;- ifelse(is.na(covs0[,5]),&quot;missing&quot;,covs0[,5])
table(covs0[,6], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##    0    1 &lt;NA&gt; 
## 7020 8670  393</code></pre>
<pre class="r"><code>covs0[,6] &lt;- ifelse(covs0[,6]==&quot; 1&quot;,&quot;yes&quot;,covs0[,6])
covs0[,6] &lt;- ifelse(covs0[,6]==&quot; 0&quot;,&quot;no&quot;,covs0[,6])
covs0[,6] &lt;- ifelse(is.na(covs0[,6]),&quot;missing&quot;,covs0[,6])
table(covs0[,7], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##                          dp                         fdc 
##                         315                        1304 
##                 independent                         nrm 
##                        2316                       10435 
## other-noincumbentpartymatch                         ufa 
##                          61                           2 
##                         upc                        &lt;NA&gt; 
##                          90                        1560</code></pre>
<pre class="r"><code>covs0[,7] &lt;- ifelse(covs0[,7]==&quot;other-noincumbentpartymatch&quot;,&quot;other&quot;,covs0[,7])
covs0[,7] &lt;- ifelse(is.na(covs0[,7]),&quot;missing&quot;,covs0[,7])
table(covs0[,8], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##              1              2              3              4              5 
##           1208            390            499           1004           2052 
##              6              7        missing not_applicable           &lt;NA&gt; 
##           2279           4701            937           3013              0</code></pre>
<pre class="r"><code>covs0[,8] &lt;- ifelse(covs0[,8]==&quot;1&quot;,&quot;1 (very little)&quot;,covs0[,8])
covs0[,8] &lt;- ifelse(covs0[,8]==&quot;7&quot;,&quot;7 (very much)&quot;,covs0[,8])
covs0[,8] &lt;- ifelse(covs0[,8]==&quot;not_applicable&quot;,&quot;no party&quot;,covs0[,8])
table(covs0[,9], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##                    don't know i_did_not_vote             no        refused 
##             39             52           2165           1955             78 
##            yes           &lt;NA&gt; 
##          11794              0</code></pre>
<pre class="r"><code>covs0[covs0==&quot;i_did_not_vote&quot;] &lt;- &quot;didn't vote&quot;
covs0[covs0==&quot;&quot;] &lt;- &quot;missing&quot;
table(covs0[,10], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
## didn't vote  don't know     missing          no     refused         yes 
##        2196          51          32        2041          85       11678 
##        &lt;NA&gt; 
##           0</code></pre>
<pre class="r"><code>table(covs0[,11], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##     don't know        missing somewhat_likel somewhat_unlik     very_likel 
##           1658            146           4864           2159           1170 
##  very_unlikely           &lt;NA&gt; 
##           6086              0</code></pre>
<pre class="r"><code>covs0[covs0==&quot;somewhat_likel&quot;] &lt;- &quot;somewhat likely&quot;
covs0[covs0==&quot;somewhat_unlik&quot;] &lt;- &quot;somewhat unlikely&quot;
covs0[covs0==&quot;very_likel&quot;] &lt;- &quot;very likely&quot;
covs0[covs0==&quot;very_unlikely&quot;] &lt;- &quot;very unlikely&quot;
table(covs0[,12], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##        missing  not_important not_very_impor somewhat_impor very_important 
##            125            693           2365           3030           9870 
##           &lt;NA&gt; 
##              0</code></pre>
<pre class="r"><code>covs0[covs0==&quot;not_important&quot;] &lt;- &quot;not important&quot;
covs0[covs0==&quot;not_very_impor&quot;] &lt;- &quot;not very important&quot;
covs0[covs0==&quot;somewhat_impor&quot;] &lt;- &quot;somewhat important&quot;
covs0[covs0==&quot;very_important&quot;] &lt;- &quot;very important&quot;
table(covs0[,13], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##      not important not very important somewhat important 
##                385               1411               2236 
##     very important               &lt;NA&gt; 
##              11925                126</code></pre>
<pre class="r"><code>covs0[is.na(covs0)] &lt;- &quot;missing&quot;
table(covs0[,14], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##            don't know local_health_services           local_roads 
##                    52                  5523                  3025 
##               missing       primary_schools     refused_to_answer 
##                    41                  2671                    53 
##          water_access                  &lt;NA&gt; 
##                  4718                     0</code></pre>
<pre class="r"><code>covs0[covs0==&quot;local_health_services&quot;] &lt;- &quot;health&quot;
covs0[covs0==&quot;local_roads&quot;] &lt;- &quot;roads&quot;
covs0[covs0==&quot;primary_schools&quot;] &lt;- &quot;schools&quot;
covs0[covs0==&quot;water_access&quot;] &lt;- &quot;water&quot;
covs0[covs0==&quot;refused_to_answer&quot;] &lt;- &quot;refused&quot;
table(covs0[,15], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##     don't know local_health_s        missing primary_school        refused 
##             65           4311             45           3887             64 
##          roads          water           &lt;NA&gt; 
##           3643           4068              0</code></pre>
<pre class="r"><code>covs0[covs0==&quot;local_health_s&quot;] &lt;- &quot;health&quot;
covs0[covs0==&quot;primary_school&quot;] &lt;- &quot;schools&quot;
table(covs0[,16], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
## do_not_trust_a     don't know        missing       option_5 trust_a_little 
##           4436            651             50            206           8365 
##    trust_a_lot           &lt;NA&gt; 
##           2375              0</code></pre>
<pre class="r"><code>covs0[covs0==&quot;do_not_trust_a&quot;] &lt;- &quot;don't trust at all&quot;
covs0[covs0==&quot;option_5&quot;] &lt;- &quot;refused&quot;
covs0[covs0==&quot;trust_a_little&quot;] &lt;- &quot;trust a little&quot;
covs0[covs0==&quot;trust_a_lot&quot;] &lt;- &quot;trust a lot&quot;
table(covs0[,17], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##         don't know don't trust at all            missing 
##               2444                608                209 
##     trust a little        trust a lot               &lt;NA&gt; 
##               7250               5572                  0</code></pre>
<pre class="r"><code>table(covs0[,18], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##         don't know don't trust at all            missing 
##               1555               1105                 66 
##            refused     trust a little        trust a lot 
##                 89               7352               5916 
##               &lt;NA&gt; 
##                  0</code></pre>
<pre class="r"><code>table(covs0[,19], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##         don't know don't trust at all            missing 
##               4742               2961                292 
##     trust a little        trust a lot               &lt;NA&gt; 
##               5885               2203                  0</code></pre>
<pre class="r"><code>table(covs0[,20], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
## a_little_worse         better     don't know        missing    much better 
##           4914           4685           3237             70            663 
##     much worse        refused           &lt;NA&gt; 
##           2386            128              0</code></pre>
<pre class="r"><code>covs0[covs0==&quot;a_little_worse&quot;] &lt;- &quot;a little worse&quot;
table(covs0[,21], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##        certain        missing    not_certain   very_certain very_uncertain 
##           6495            228           4755           3966            639 
##           &lt;NA&gt; 
##              0</code></pre>
<pre class="r"><code>covs0[covs0==&quot;not_certain&quot;] &lt;- &quot;not certain&quot;
covs0[covs0==&quot;very_certain&quot;] &lt;- &quot;very certain&quot;
covs0[covs0==&quot;very_uncertain&quot;] &lt;- &quot;very uncertain&quot;
table(covs0[,22], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##      don't know         missing  not_at_all_lik  not_very_likel 
##            1480              60            6962            3395 
##         refused somewhat likely     very_likely            &lt;NA&gt; 
##             192            2002            1992               0</code></pre>
<pre class="r"><code>covs0[covs0==&quot;not_at_all_lik&quot;] &lt;- &quot;not at all likely&quot;
covs0[covs0==&quot;not_very_likel&quot;] &lt;- &quot;not very likely&quot;
covs0[covs0==&quot;very_likely&quot;] &lt;- &quot;very likely&quot;
table(covs0[,23], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##        don't know           missing not at all likely   not very likely 
##              2403                67              2006              1757 
##           refused   somewhat likely       very likely              &lt;NA&gt; 
##               242              2917              6691                 0</code></pre>
<pre class="r"><code>table(covs0[,24], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
## female   male   &lt;NA&gt; 
##   5370  10713      0</code></pre>
<pre class="r"><code>table(covs0[,25], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
## completed_mast completed_prim completed_seco completed_univ   no_schooling 
##              6           3196           1229            386            687 
## refuse_to_answ some_post_grad some_primary_s some_secondary some_universit 
##             10             75           4384           5445            665 
##           &lt;NA&gt; 
##              0</code></pre>
<pre class="r"><code>covs0[covs0==&quot;completed_mast&quot;] &lt;- &quot;complete graduate&quot;
covs0[covs0==&quot;completed_prim&quot;] &lt;- &quot;complete primary&quot;
covs0[covs0==&quot;completed_seco&quot;] &lt;- &quot;complete secondary&quot;
covs0[covs0==&quot;completed_univ&quot;] &lt;- &quot;complete university&quot;
covs0[covs0==&quot;no_schooling&quot;] &lt;- &quot;no schooling&quot;
covs0[covs0==&quot;refuse_to_answ&quot;] &lt;- &quot;refused&quot;
covs0[covs0==&quot;some_post_grad&quot;] &lt;- &quot;some graduate&quot;
covs0[covs0==&quot;some_primary_s&quot;] &lt;- &quot;some primary&quot;
covs0[covs0==&quot;some_secondary&quot;] &lt;- &quot;some secondary&quot;
covs0[covs0==&quot;some_universit&quot;] &lt;- &quot;some university&quot;
table(covs0[,26], useNA=&quot;always&quot;) #Rename these in letters</code></pre>
<pre><code>## 
##        alur       ateso   AUTOMATIC     english      french  japhadhola 
##         315         714          42        4454          10           5 
##       kakwa  karamojong   kiswahili       kumam       langi     luganda 
##           1         107          60          13         930        5673 
##     lugbara      lugisu     lugwere    lukhonjo      lululi    lumasaba 
##         235         229          97         326         100          23 
##     lusamia      lusoga      nubian  portuguese      refuse   rufumbira 
##           4         243           2           2           1         460 
##      rukiga  runyankole runyarwanda     runyolo     rutooro      sabiny 
##         229        1148          23         418         215           4 
##        &lt;NA&gt; 
##           0</code></pre>
<pre class="r"><code>covs0[,26] &lt;- ifelse(covs0[,26]!=&quot;luganda&quot; &amp; covs0[,26]!=&quot;english&quot; &amp; covs0[,26]!=&quot;runyankole&quot; &amp; covs0[,26]!=&quot;langi&quot; &amp; covs0[,26]!=&quot;ateso&quot;, &quot;other&quot;, covs0[,26])
covs0[covs0==&quot;AUTOMATIC&quot;] &lt;- &quot;other&quot;
table(covs0[,27], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32 
##  456  345  543  360  457  571  504  697  519  485  743  447  916  242  623 
##   33   34   35   36   37   38   39   40   41   42   43   44   45   46   47 
##  297  299  680  393  266  508  239  768  196  447  261  170  564  175  185 
##   48   49   50   51   52   53   54   55   56   57   58   59   60   61   62 
##  264  159  400  112  209  142  133  123  128   76  106   48  242   32   61 
##   63   64   65   66   67   68   69   70   71   72   73   74   75   76   77 
##   59   40   92   26   46   28   17   65   10   19   15    9   22    6    5 
##   78   79   80   81   82   83   84   85   87   89   92   98 &lt;NA&gt; 
##    5    4    8    2    1    4    3    2    1    1    1    1    0</code></pre>
<pre class="r"><code>##Creating factors with appropriate levels
table(covs0[,2], useNA=&quot;always&quot;)</code></pre>
<pre><code>## 
##      better  don't know     missing much better  much worse     refused 
##        4993         182          24         422        1127          35 
##        same       worse        &lt;NA&gt; 
##        3983        5317           0</code></pre>
<pre class="r"><code>covs0[,2] &lt;- factor(covs0[,2], levels=rev(c(&quot;much better&quot;,&quot;better&quot;,&quot;same&quot;,&quot;worse&quot;,&quot;much worse&quot;,&quot;don't know&quot;,&quot;missing&quot;,&quot;refused&quot;)))
sort(table(covs0[,7]))</code></pre>
<pre><code>## 
##         ufa       other         upc          dp         fdc     missing 
##           2          61          90         315        1304        1560 
## independent         nrm 
##        2316       10435</code></pre>
<pre class="r"><code>covs0[,7] &lt;- factor(covs0[,7], levels=c(&quot;missing&quot;,&quot;ufa&quot;,&quot;other&quot;,&quot;upc&quot;,&quot;dp&quot;,&quot;fdc&quot;,&quot;independent&quot;,&quot;nrm&quot;))
covs0[,8] &lt;- factor(covs0[,8], levels=rev(c(&quot;7 (very much)&quot;,&quot;6&quot;,&quot;5&quot;,&quot;4&quot;,&quot;3&quot;,&quot;2&quot;,&quot;1 (very little)&quot;,&quot;no party&quot;,&quot;missing&quot;)))
covs0[,9] &lt;- factor(covs0[,9], levels=c(&quot;missing&quot;,&quot;refused&quot;,&quot;don't know&quot;,&quot;didn't vote&quot;,&quot;no&quot;,&quot;yes&quot;))
covs0[,10] &lt;- factor(covs0[,10], levels=c(&quot;missing&quot;,&quot;refused&quot;,&quot;don't know&quot;,&quot;didn't vote&quot;,&quot;no&quot;,&quot;yes&quot;))
covs0[,11] &lt;- factor(covs0[,11], levels=c(&quot;missing&quot;,&quot;don't know&quot;,&quot;very likely&quot;,&quot;somewhat likely&quot;,&quot;somewhat unlikely&quot;,&quot;very unlikely&quot;))
covs0[,14] &lt;- factor(covs0[,14], levels=c(&quot;missing&quot;,&quot;refused&quot;,&quot;don't know&quot;,&quot;schools&quot;,&quot;roads&quot;,&quot;water&quot;,&quot;health&quot;))
covs0[,15] &lt;- factor(covs0[,15], levels=c(&quot;missing&quot;,&quot;refused&quot;,&quot;don't know&quot;,&quot;schools&quot;,&quot;roads&quot;,&quot;water&quot;,&quot;health&quot;))
covs0[,16] &lt;- factor(covs0[,16], levels=c(&quot;missing&quot;,&quot;refused&quot;,&quot;don't know&quot;,&quot;don't trust at all&quot;,&quot;trust a little&quot;,&quot;trust a lot&quot;))
covs0[,17] &lt;- factor(covs0[,17], levels=c(&quot;missing&quot;,&quot;refused&quot;,&quot;don't know&quot;,&quot;don't trust at all&quot;,&quot;trust a little&quot;,&quot;trust a lot&quot;))
covs0[,18] &lt;- factor(covs0[,18], levels=c(&quot;missing&quot;,&quot;refused&quot;,&quot;don't know&quot;,&quot;don't trust at all&quot;,&quot;trust a little&quot;,&quot;trust a lot&quot;))
covs0[,19] &lt;- factor(covs0[,19], levels=c(&quot;missing&quot;,&quot;refused&quot;,&quot;don't know&quot;,&quot;don't trust at all&quot;,&quot;trust a little&quot;,&quot;trust a lot&quot;))
covs0[,20] &lt;- factor(covs0[,20], levels=c(&quot;missing&quot;,&quot;refused&quot;,&quot;much worse&quot;,&quot;a little worse&quot;,&quot;don't know&quot;,&quot;better&quot;,&quot;much better&quot;))
covs0[,21] &lt;- factor(covs0[,21], levels=c(&quot;missing&quot;,&quot;very uncertain&quot;,&quot;not certain&quot;,&quot;certain&quot;,&quot;very certain&quot;))
covs0[,22] &lt;- factor(covs0[,22], levels=c(&quot;missing&quot;,&quot;refused&quot;,&quot;don't know&quot;,&quot;not at all likely&quot;,&quot;not very likely&quot;,&quot;somewhat likely&quot;,&quot;very likely&quot;))
covs0[,23] &lt;- factor(covs0[,23], levels=c(&quot;missing&quot;,&quot;refused&quot;,&quot;don't know&quot;,&quot;not at all likely&quot;,&quot;not very likely&quot;,&quot;somewhat likely&quot;,&quot;very likely&quot;))
covs0[,25] &lt;- factor(covs0[,25], levels=c(&quot;refused&quot;,&quot;no schooling&quot;,&quot;some primary&quot;,&quot;complete primary&quot;,&quot;some secondary&quot;,&quot;complete secondary&quot;,&quot;some university&quot;,&quot;complete university&quot;,&quot;some graduate&quot;,&quot;complete graduate&quot;))
covs0[,26] &lt;- factor(covs0[,26], levels=c(&quot;other&quot;,&quot;ateso&quot;,&quot;langi&quot;,&quot;runyankole&quot;,&quot;english&quot;,&quot;luganda&quot;))

btab &lt;- bal.tab(covs0[2:27], treat=covs0$budget.treat, disp.means=TRUE, quick=TRUE, s.d.denom=&quot;control&quot;)

options(scipen=999)

balance=btab$Balance
balance$id=(1:nrow(balance))
balance$treat = balance$M.1.Un
balance$control = balance$M.0.Un
balance$difference = balance$Diff.Un
balance=balance[,c(&quot;treat&quot;, &quot;control&quot;, &quot;difference&quot;)]
balance &lt;- balance[rev(order(balance$difference)),]

N=16083
N.t &lt;- 8064
N.c &lt;- 8019
balance$v1=((balance$treat)*(1-balance$treat)/N.t)
balance$v2=((balance$control)*(1-balance$control)/N.c)
balance$v3=balance$v1+balance$v2

balance$treat=paste(round(balance$treat, 4), &quot;  (&quot;, round(sqrt(balance$v1),4), &quot;)&quot;, sep=&quot;&quot;)
balance$control=paste(round(balance$control, 4), &quot;  (&quot;, round(sqrt(balance$v2),4), &quot;)&quot;, sep=&quot;&quot;)
balance$difference=paste(round(balance$difference, 4), &quot;  (&quot;, round(sqrt(balance$v3),4), &quot;)&quot;, sep=&quot;&quot;)

balance$id=NULL
balance$v1=NULL
balance$v2=NULL
balance$v3=NULL

#Manually computing standard errors for the one continuous variable, entered manually in table
age.treat.se &lt;- sd(covs0$Age[covs0$budget.treat==1])/sqrt(length(covs0$Age[covs0$budget.treat==1]))
age.control.se &lt;- sd(covs0$Age[covs0$budget.treat==0])/sqrt(length(covs0$Age[covs0$budget.treat==0]))
age.diff.se &lt;- sqrt(age.treat.se+age.control.se)

xtable(balance)</code></pre>
<pre><code>## % latex table generated in R 3.5.0 by xtable 1.8-2 package
## % Fri Apr 27 11:19:07 2018
## \begin{table}[ht]
## \centering
## \begin{tabular}{rlll}
##   \hline
##  &amp; treat &amp; control &amp; difference \\ 
##   \hline
## Councillor Vote Intent\_no &amp; 0.4252  (0.0055) &amp; 0.411  (0.0055) &amp; 0.0142  (0.0078) \\ 
##   Second Important Service\_water &amp; 0.2593  (0.0049) &amp; 0.2465  (0.0048) &amp; 0.0128  (0.0069) \\ 
##   Voting Secret\_not very likely &amp; 0.2166  (0.0046) &amp; 0.2055  (0.0045) &amp; 0.0111  (0.0064) \\ 
##   Most Important Service\_health &amp; 0.3488  (0.0053) &amp; 0.3379  (0.0053) &amp; 0.0109  (0.0075) \\ 
##   Language\_other &amp; 0.2019  (0.0045) &amp; 0.1915  (0.0044) &amp; 0.0103  (0.0063) \\ 
##   Vote Buying\_somewhat likely &amp; 0.3074  (0.0051) &amp; 0.2974  (0.0051) &amp; 0.01  (0.0072) \\ 
##   Living Conditions\_same &amp; 0.2524  (0.0048) &amp; 0.2429  (0.0048) &amp; 0.0094  (0.0068) \\ 
##   Trust Local Politicians\_don't trust at all &amp; 0.2801  (0.005) &amp; 0.2715  (0.005) &amp; 0.0087  (0.007) \\ 
##   Voted for Chair 2011\_no &amp; 0.1312  (0.0038) &amp; 0.1226  (0.0037) &amp; 0.0086  (0.0052) \\ 
##   Vote Count Fair\_somewhat likely &amp; 0.1855  (0.0043) &amp; 0.1772  (0.0043) &amp; 0.0083  (0.0061) \\ 
##   Services Importance\_very important &amp; 0.7455  (0.0049) &amp; 0.7374  (0.0049) &amp; 0.0082  (0.0069) \\ 
##   Education\_complete secondary &amp; 0.0799  (0.003) &amp; 0.073  (0.0029) &amp; 0.0069  (0.0042) \\ 
##   Trust Auditor General\_don't trust at all &amp; 0.1875  (0.0043) &amp; 0.1807  (0.0043) &amp; 0.0068  (0.0061) \\ 
##   Voted for Councillor 2011\_no &amp; 0.1246  (0.0037) &amp; 0.1185  (0.0036) &amp; 0.0062  (0.0052) \\ 
##   Education\_complete primary &amp; 0.2018  (0.0045) &amp; 0.1957  (0.0044) &amp; 0.0061  (0.0063) \\ 
##   Party\_nrm &amp; 0.6517  (0.0053) &amp; 0.646  (0.0053) &amp; 0.0057  (0.0075) \\ 
##   Vote Buying\_somewhat unlikely &amp; 0.137  (0.0038) &amp; 0.1314  (0.0038) &amp; 0.0056  (0.0054) \\ 
##   Language\_langi &amp; 0.0605  (0.0027) &amp; 0.0551  (0.0025) &amp; 0.0054  (0.0037) \\ 
##   Party Attachment\_7 (very much) &amp; 0.2949  (0.0051) &amp; 0.2897  (0.0051) &amp; 0.0052  (0.0072) \\ 
##   Chair Vote Intent\_missing &amp; 0.0269  (0.0018) &amp; 0.0219  (0.0016) &amp; 0.005  (0.0024) \\ 
##   Budget Importance\_somewhat important &amp; 0.1907  (0.0044) &amp; 0.1861  (0.0043) &amp; 0.0047  (0.0062) \\ 
##   Language\_runyankole &amp; 0.0737  (0.0029) &amp; 0.0691  (0.0028) &amp; 0.0046  (0.0041) \\ 
##   Party\_missing &amp; 0.0991  (0.0033) &amp; 0.0949  (0.0033) &amp; 0.0042  (0.0047) \\ 
##   Vote Count Fair\_very likely &amp; 0.418  (0.0055) &amp; 0.414  (0.0055) &amp; 0.004  (0.0078) \\ 
##   Party Attachment\_5 &amp; 0.1296  (0.0037) &amp; 0.1256  (0.0037) &amp; 0.004  (0.0053) \\ 
##   Living Conditions\_worse &amp; 0.3326  (0.0052) &amp; 0.3286  (0.0052) &amp; 0.004  (0.0074) \\ 
##   Councillor Vote Intent\_missing &amp; 0.0248  (0.0017) &amp; 0.021  (0.0016) &amp; 0.0039  (0.0024) \\ 
##   Party Attachment\_missing &amp; 0.06  (0.0026) &amp; 0.0565  (0.0026) &amp; 0.0035  (0.0037) \\ 
##   Budget Prior Certainty\_missing &amp; 0.0159  (0.0014) &amp; 0.0125  (0.0012) &amp; 0.0034  (0.0019) \\ 
##   Party Attachment\_1 (very little) &amp; 0.0768  (0.003) &amp; 0.0735  (0.0029) &amp; 0.0033  (0.0042) \\ 
##   Trust NGOs\_trust a little &amp; 0.4587  (0.0055) &amp; 0.4555  (0.0056) &amp; 0.0032  (0.0079) \\ 
##   Vote Buying\_very likely &amp; 0.0743  (0.0029) &amp; 0.0712  (0.0029) &amp; 0.0031  (0.0041) \\ 
##   Trust Twaweza\_trust a little &amp; 0.4521  (0.0055) &amp; 0.4494  (0.0056) &amp; 0.0027  (0.0078) \\ 
##   Party\_fdc &amp; 0.0823  (0.0031) &amp; 0.0798  (0.003) &amp; 0.0025  (0.0043) \\ 
##   Voting Secret\_refused &amp; 0.0131  (0.0013) &amp; 0.0107  (0.0012) &amp; 0.0024  (0.0017) \\ 
##   Trust Local Politicians\_refused &amp; 0.014  (0.0013) &amp; 0.0116  (0.0012) &amp; 0.0024  (0.0018) \\ 
##   Trust Auditor General\_missing &amp; 0.0193  (0.0015) &amp; 0.017  (0.0014) &amp; 0.0024  (0.0021) \\ 
##   Voting Secret\_very likely &amp; 0.125  (0.0037) &amp; 0.1227  (0.0037) &amp; 0.0023  (0.0052) \\ 
##   Budget Prior\_refused &amp; 0.0091  (0.0011) &amp; 0.0069  (0.0009) &amp; 0.0022  (0.0014) \\ 
##   Same Tribe Councillor\_yes &amp; 0.714  (0.005) &amp; 0.7119  (0.0051) &amp; 0.0021  (0.0071) \\ 
##   Party\_other &amp; 0.0048  (0.0008) &amp; 0.0027  (0.0006) &amp; 0.0021  (0.001) \\ 
##   Budget Prior Certainty\_certain &amp; 0.4049  (0.0055) &amp; 0.4028  (0.0055) &amp; 0.0021  (0.0077) \\ 
##   Budget Prior\_a little worse &amp; 0.3065  (0.0051) &amp; 0.3045  (0.0051) &amp; 0.002  (0.0073) \\ 
##   Voted for Councillor 2011\_didn't vote &amp; 0.1355  (0.0038) &amp; 0.1337  (0.0038) &amp; 0.0019  (0.0054) \\ 
##   Trust Twaweza\_missing &amp; 0.0139  (0.0013) &amp; 0.0121  (0.0012) &amp; 0.0018  (0.0018) \\ 
##   Budget Importance\_very important &amp; 0.6146  (0.0054) &amp; 0.6128  (0.0054) &amp; 0.0018  (0.0077) \\ 
##   Language\_ateso &amp; 0.0453  (0.0023) &amp; 0.0435  (0.0023) &amp; 0.0017  (0.0032) \\ 
##   Most Important Service\_schools &amp; 0.1669  (0.0042) &amp; 0.1652  (0.0041) &amp; 0.0017  (0.0059) \\ 
##   Voted for Councillor 2011\_refused &amp; 0.0056  (0.0008) &amp; 0.0041  (0.0007) &amp; 0.0015  (0.0011) \\ 
##   Education\_some secondary &amp; 0.3393  (0.0053) &amp; 0.3378  (0.0053) &amp; 0.0015  (0.0075) \\ 
##   Chair Vote Intent\_no &amp; 0.4371  (0.0055) &amp; 0.4358  (0.0055) &amp; 0.0013  (0.0078) \\ 
##   Trust Twaweza\_don't trust at all &amp; 0.0384  (0.0021) &amp; 0.0372  (0.0021) &amp; 0.0013  (0.003) \\ 
##   Budget Prior\_much worse &amp; 0.1489  (0.004) &amp; 0.1478  (0.004) &amp; 0.0012  (0.0056) \\ 
##   Vote Count Fair\_refused &amp; 0.0156  (0.0014) &amp; 0.0145  (0.0013) &amp; 0.0012  (0.0019) \\ 
##   Trust Twaweza\_don't know &amp; 0.1525  (0.004) &amp; 0.1514  (0.004) &amp; 0.0011  (0.0057) \\ 
##   Trust Local Politicians\_missing &amp; 0.0036  (0.0007) &amp; 0.0026  (0.0006) &amp; 0.001  (0.0009) \\ 
##   Party\_upc &amp; 0.0061  (0.0009) &amp; 0.0051  (0.0008) &amp; 0.001  (0.0012) \\ 
##   Budget Prior Certainty\_very uncertain &amp; 0.0402  (0.0022) &amp; 0.0393  (0.0022) &amp; 0.0009  (0.0031) \\ 
##   Second Important Service\_roads &amp; 0.2269  (0.0047) &amp; 0.2261  (0.0047) &amp; 0.0008  (0.0066) \\ 
##   Trust NGOs\_don't know &amp; 0.0971  (0.0033) &amp; 0.0963  (0.0033) &amp; 0.0008  (0.0047) \\ 
##   Voted for Chair 2011\_didn't vote &amp; 0.1369  (0.0038) &amp; 0.1362  (0.0038) &amp; 0.0007  (0.0054) \\ 
##   Most Important Service\_don't know &amp; 0.0036  (0.0007) &amp; 0.0029  (0.0006) &amp; 0.0007  (0.0009) \\ 
##   Services Importance\_somewhat important &amp; 0.1394  (0.0039) &amp; 0.1387  (0.0039) &amp; 0.0007  (0.0055) \\ 
##   Vote Buying\_missing &amp; 0.0094  (0.0011) &amp; 0.0087  (0.001) &amp; 0.0007  (0.0015) \\ 
##   Living Conditions\_refused &amp; 0.0025  (0.0006) &amp; 0.0019  (0.0005) &amp; 0.0006  (0.0007) \\ 
##   Education\_some graduate &amp; 0.005  (0.0008) &amp; 0.0044  (0.0007) &amp; 0.0006  (0.0011) \\ 
##   Turnout Intent\_yes &amp; 0.9391  (0.0027) &amp; 0.9385  (0.0027) &amp; 0.0006  (0.0038) \\ 
##   Trust NGOs\_refused &amp; 0.0058  (0.0008) &amp; 0.0052  (0.0008) &amp; 0.0006  (0.0012) \\ 
##   Party Attachment\_6 &amp; 0.142  (0.0039) &amp; 0.1414  (0.0039) &amp; 0.0006  (0.0055) \\ 
##   Trust Local Politicians\_trust a lot &amp; 0.1479  (0.004) &amp; 0.1474  (0.004) &amp; 0.0005  (0.0056) \\ 
##   Trust NGOs\_missing &amp; 0.0043  (0.0007) &amp; 0.0039  (0.0007) &amp; 0.0005  (0.001) \\ 
##   Budget Prior Certainty\_very certain &amp; 0.2468  (0.0048) &amp; 0.2464  (0.0048) &amp; 0.0004  (0.0068) \\ 
##   Living Conditions\_much better &amp; 0.0264  (0.0018) &amp; 0.0261  (0.0018) &amp; 0.0004  (0.0025) \\ 
##   Second Important Service\_don't know &amp; 0.0042  (0.0007) &amp; 0.0039  (0.0007) &amp; 0.0004  (0.001) \\ 
##   Party\_ufa &amp; 0.0002  (0.0002) &amp; 0  (0) &amp; 0.0002  (0.0002) \\ 
##   Same Tribe Councillor\_missing &amp; 0.0016  (0.0004) &amp; 0.0014  (0.0004) &amp; 0.0002  (0.0006) \\ 
##   Budget Prior\_missing &amp; 0.0045  (0.0007) &amp; 0.0042  (0.0007) &amp; 0.0002  (0.001) \\ 
##   Living Conditions\_don't know &amp; 0.0114  (0.0012) &amp; 0.0112  (0.0012) &amp; 0.0002  (0.0017) \\ 
##   Party Attachment\_4 &amp; 0.0625  (0.0027) &amp; 0.0624  (0.0027) &amp; 0.0001  (0.0038) \\ 
##   Voted for Chair 2011\_missing &amp; 0.002  (0.0005) &amp; 0.002  (0.0005) &amp; 0  (0.0007) \\ 
##   Budget Prior\_better &amp; 0.2913  (0.0051) &amp; 0.2913  (0.0051) &amp; 0  (0.0072) \\ 
##   Most Important Service\_missing &amp; 0.0025  (0.0006) &amp; 0.0026  (0.0006) &amp; -0.0001  (0.0008) \\ 
##   Turnout Intent\_missing &amp; 0.0033  (0.0006) &amp; 0.0035  (0.0007) &amp; -0.0001  (0.0009) \\ 
##   Voted for Councillor 2011\_missing &amp; 0.0022  (0.0005) &amp; 0.0026  (0.0006) &amp; -0.0004  (0.0008) \\ 
##   Second Important Service\_missing &amp; 0.0026  (0.0006) &amp; 0.003  (0.0006) &amp; -0.0004  (0.0008) \\ 
##   Most Important Service\_refused &amp; 0.0031  (0.0006) &amp; 0.0035  (0.0007) &amp; -0.0004  (0.0009) \\ 
##   Trust Auditor General\_trust a lot &amp; 0.1368  (0.0038) &amp; 0.1372  (0.0038) &amp; -0.0004  (0.0054) \\ 
##   Turnout Intent\_no &amp; 0.0575  (0.0026) &amp; 0.058  (0.0026) &amp; -0.0004  (0.0037) \\ 
##   Education\_complete graduate &amp; 0.0001  (0.0001) &amp; 0.0006  (0.0003) &amp; -0.0005  (0.0003) \\ 
##   Second Important Service\_refused &amp; 0.0037  (0.0007) &amp; 0.0042  (0.0007) &amp; -0.0005  (0.001) \\ 
##   Education\_no schooling &amp; 0.0424  (0.0022) &amp; 0.043  (0.0023) &amp; -0.0006  (0.0032) \\ 
##   Second Important Service\_schools &amp; 0.2413  (0.0048) &amp; 0.2421  (0.0048) &amp; -0.0007  (0.0068) \\ 
##   Vote Count Fair\_missing &amp; 0.0037  (0.0007) &amp; 0.0046  (0.0008) &amp; -0.0009  (0.001) \\ 
##   Education\_refused &amp; 0.0001  (0.0001) &amp; 0.0011  (0.0004) &amp; -0.001  (0.0004) \\ 
##   Living Conditions\_missing &amp; 0.001  (0.0004) &amp; 0.002  (0.0005) &amp; -0.001  (0.0006) \\ 
##   Budget Importance\_missing &amp; 0.0072  (0.0009) &amp; 0.0084  (0.001) &amp; -0.0012  (0.0014) \\ 
##   Trust NGOs\_don't trust at all &amp; 0.0681  (0.0028) &amp; 0.0693  (0.0028) &amp; -0.0013  (0.004) \\ 
##   Voted for Councillor 2011\_don't know &amp; 0.0026  (0.0006) &amp; 0.0039  (0.0007) &amp; -0.0013  (0.0009) \\ 
##   Services Importance\_missing &amp; 0.0072  (0.0009) &amp; 0.0085  (0.001) &amp; -0.0013  (0.0014) \\ 
##   Vote Count Fair\_not very likely &amp; 0.1085  (0.0035) &amp; 0.11  (0.0035) &amp; -0.0015  (0.0049) \\ 
##   Budget Prior\_don't know &amp; 0.2005  (0.0045) &amp; 0.202  (0.0045) &amp; -0.0015  (0.0063) \\ 
##   Party\_dp &amp; 0.0186  (0.0015) &amp; 0.0206  (0.0016) &amp; -0.002  (0.0022) \\ 
##   Trust Auditor General\_don't know &amp; 0.2938  (0.0051) &amp; 0.2959  (0.0051) &amp; -0.0021  (0.0072) \\ 
##   Budget Importance\_not very important &amp; 0.146  (0.0039) &amp; 0.1481  (0.004) &amp; -0.0022  (0.0056) \\ 
##   Voting Secret\_don't know &amp; 0.0909  (0.0032) &amp; 0.0932  (0.0032) &amp; -0.0023  (0.0046) \\ 
##   Same Tribe Councillor\_no &amp; 0.2844  (0.005) &amp; 0.2867  (0.005) &amp; -0.0023  (0.0071) \\ 
##   Education\_some university &amp; 0.0401  (0.0022) &amp; 0.0426  (0.0023) &amp; -0.0026  (0.0031) \\ 
##   Party Attachment\_3 &amp; 0.0295  (0.0019) &amp; 0.0325  (0.002) &amp; -0.003  (0.0027) \\ 
##   Budget Importance\_not important &amp; 0.0415  (0.0022) &amp; 0.0446  (0.0023) &amp; -0.0031  (0.0032) \\ 
##   Services Importance\_not important &amp; 0.0222  (0.0016) &amp; 0.0257  (0.0018) &amp; -0.0035  (0.0024) \\ 
##   Trust NGOs\_trust a lot &amp; 0.3659  (0.0054) &amp; 0.3697  (0.0054) &amp; -0.0038  (0.0076) \\ 
##   Budget Prior\_much better &amp; 0.0392  (0.0022) &amp; 0.0433  (0.0023) &amp; -0.0041  (0.0031) \\ 
##   Services Importance\_not very important &amp; 0.0857  (0.0031) &amp; 0.0898  (0.0032) &amp; -0.0041  (0.0045) \\ 
##   Party Attachment\_2 &amp; 0.0222  (0.0016) &amp; 0.0263  (0.0018) &amp; -0.0041  (0.0024) \\ 
##   Education\_some primary &amp; 0.2705  (0.0049) &amp; 0.2747  (0.005) &amp; -0.0043  (0.007) \\ 
##   Vote Count Fair\_not at all likely &amp; 0.1224  (0.0036) &amp; 0.1271  (0.0037) &amp; -0.0047  (0.0052) \\ 
##   Trust Local Politicians\_don't know &amp; 0.0381  (0.0021) &amp; 0.0429  (0.0023) &amp; -0.0048  (0.0031) \\ 
##   Most Important Service\_roads &amp; 0.1856  (0.0043) &amp; 0.1905  (0.0044) &amp; -0.0049  (0.0062) \\ 
##   Gender\_female &amp; 0.3313  (0.0052) &amp; 0.3365  (0.0053) &amp; -0.0051  (0.0074) \\ 
##   Voting Secret\_not at all likely &amp; 0.4302  (0.0055) &amp; 0.4356  (0.0055) &amp; -0.0054  (0.0078) \\ 
##   Living Conditions\_much worse &amp; 0.0672  (0.0028) &amp; 0.073  (0.0029) &amp; -0.0057  (0.004) \\ 
##   Education\_complete university &amp; 0.021  (0.0016) &amp; 0.0271  (0.0018) &amp; -0.0061  (0.0024) \\ 
##   Chair Vote Intent\_yes &amp; 0.536  (0.0056) &amp; 0.5422  (0.0056) &amp; -0.0062  (0.0079) \\ 
##   Vote Count Fair\_don't know &amp; 0.1462  (0.0039) &amp; 0.1526  (0.004) &amp; -0.0064  (0.0056) \\ 
##   Trust Auditor General\_trust a little &amp; 0.3626  (0.0054) &amp; 0.3692  (0.0054) &amp; -0.0066  (0.0076) \\ 
##   Budget Prior Certainty\_not certain &amp; 0.2923  (0.0051) &amp; 0.299  (0.0051) &amp; -0.0068  (0.0072) \\ 
##   Trust Twaweza\_trust a lot &amp; 0.343  (0.0053) &amp; 0.3499  (0.0053) &amp; -0.0069  (0.0075) \\ 
##   Trust Local Politicians\_trust a little &amp; 0.5162  (0.0056) &amp; 0.524  (0.0056) &amp; -0.0078  (0.0079) \\ 
##   Living Conditions\_better &amp; 0.3065  (0.0051) &amp; 0.3144  (0.0052) &amp; -0.0078  (0.0073) \\ 
##   Voted for Councillor 2011\_yes &amp; 0.7294  (0.0049) &amp; 0.7372  (0.0049) &amp; -0.0078  (0.007) \\ 
##   Most Important Service\_water &amp; 0.2894  (0.0051) &amp; 0.2973  (0.0051) &amp; -0.0079  (0.0072) \\ 
##   Voting Secret\_somewhat likely &amp; 0.1204  (0.0036) &amp; 0.1286  (0.0037) &amp; -0.0082  (0.0052) \\ 
##   Vote Buying\_don't know &amp; 0.0985  (0.0033) &amp; 0.1077  (0.0035) &amp; -0.0093  (0.0048) \\ 
##   Party Attachment\_no party &amp; 0.1825  (0.0043) &amp; 0.1922  (0.0044) &amp; -0.0096  (0.0062) \\ 
##   Language\_luganda &amp; 0.3478  (0.0053) &amp; 0.3577  (0.0054) &amp; -0.0098  (0.0075) \\ 
##   Vote Buying\_very unlikely &amp; 0.3734  (0.0054) &amp; 0.3835  (0.0054) &amp; -0.0101  (0.0076) \\ 
##   Voted for Chair 2011\_yes &amp; 0.721  (0.005) &amp; 0.7313  (0.005) &amp; -0.0103  (0.007) \\ 
##   Language\_english &amp; 0.2708  (0.0049) &amp; 0.2831  (0.005) &amp; -0.0122  (0.0071) \\ 
##   Second Important Service\_health &amp; 0.2619  (0.0049) &amp; 0.2742  (0.005) &amp; -0.0123  (0.007) \\ 
##   Party\_independent &amp; 0.1372  (0.0038) &amp; 0.1509  (0.004) &amp; -0.0137  (0.0055) \\ 
##   Age &amp; 35.2681  (NaN) &amp; 35.4751  (NaN) &amp; -0.0169  (NaN) \\ 
##   Councillor Vote Intent\_yes &amp; 0.55  (0.0055) &amp; 0.568  (0.0055) &amp; -0.0181  (0.0078) \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>options(scipen=0)


######################################################################################
###Table S7: Distribution of voter party identification, politician party identification, and alignment by news group for main estimation
######################################################################################

budget.good.lc5.chair.comp.complete &lt;- subset(budget.good.lc5.chair.comp, !is.na(lc5.chair.inc.vote) &amp; !is.na(budget.treat) &amp; !is.na(lc5.chair.intent) &amp; !is.na(lc5.chair.name))
budget.good.lc5.councillor.comp.complete &lt;- subset(budget.good.lc5.councillor.comp, !is.na(lc5.councillor.inc.vote) &amp; !is.na(budget.treat) &amp; !is.na(lc5.councillor.intent) &amp; !is.na(lc5.councillor.name))
budget.bad.lc5.chair.comp.complete &lt;- subset(budget.bad.lc5.chair.comp, !is.na(lc5.chair.inc.vote) &amp; !is.na(budget.treat) &amp; !is.na(lc5.chair.intent) &amp; !is.na(lc5.chair.name))
budget.bad.lc5.councillor.comp.complete &lt;- subset(budget.bad.lc5.councillor.comp, !is.na(lc5.councillor.inc.vote) &amp; !is.na(budget.treat) &amp; !is.na(lc5.councillor.intent) &amp; !is.na(lc5.councillor.name))

budget.good.lc5.chair.comp.complete$partyID=ifelse(budget.good.lc5.chair.comp.complete$partyID==&quot;upc&quot;, &quot;other&quot;, budget.good.lc5.chair.comp.complete$partyID)
budget.good.lc5.chair.comp.complete$partyID=ifelse(budget.good.lc5.chair.comp.complete$partyID==&quot;ufa&quot;, &quot;other&quot;, budget.good.lc5.chair.comp.complete$partyID)
budget.good.lc5.chair.comp.complete$partyID=ifelse(budget.good.lc5.chair.comp.complete$partyID==&quot;other-noincumbentpartymatch&quot;, &quot;other&quot;, budget.good.lc5.chair.comp.complete$partyID)
xtabs(~budget.good.lc5.chair.comp.complete$partyID)</code></pre>
<pre><code>## budget.good.lc5.chair.comp.complete$partyID
##          dp         fdc independent         nrm       other 
##         114         360         638        2432          34</code></pre>
<pre class="r"><code>budget.good.lc5.councillor.comp.complete$partyID=ifelse(budget.good.lc5.councillor.comp.complete$partyID==&quot;upc&quot;, &quot;other&quot;, budget.good.lc5.councillor.comp.complete$partyID)
budget.good.lc5.councillor.comp.complete$partyID=ifelse(budget.good.lc5.councillor.comp.complete$partyID==&quot;ufa&quot;, &quot;other&quot;, budget.good.lc5.councillor.comp.complete$partyID)
budget.good.lc5.councillor.comp.complete$partyID=ifelse(budget.good.lc5.councillor.comp.complete$partyID==&quot;other-noincumbentpartymatch&quot;, &quot;other&quot;, budget.good.lc5.councillor.comp.complete$partyID)
xtabs(~budget.good.lc5.councillor.comp.complete$partyID)</code></pre>
<pre><code>## budget.good.lc5.councillor.comp.complete$partyID
##          dp         fdc independent         nrm       other 
##          91         292         442        1970          27</code></pre>
<pre class="r"><code>budget.bad.lc5.chair.comp.complete$partyID=ifelse(budget.bad.lc5.chair.comp.complete$partyID==&quot;upc&quot;, &quot;other&quot;, budget.bad.lc5.chair.comp.complete$partyID)
budget.bad.lc5.chair.comp.complete$partyID=ifelse(budget.bad.lc5.chair.comp.complete$partyID==&quot;ufa&quot;, &quot;other&quot;, budget.bad.lc5.chair.comp.complete$partyID)
budget.bad.lc5.chair.comp.complete$partyID=ifelse(budget.bad.lc5.chair.comp.complete$partyID==&quot;other-noincumbentpartymatch&quot;, &quot;other&quot;, budget.bad.lc5.chair.comp.complete$partyID)
xtabs(~budget.bad.lc5.chair.comp.complete$partyID)</code></pre>
<pre><code>## budget.bad.lc5.chair.comp.complete$partyID
##          dp         fdc independent         nrm       other 
##          44         219         378        2290          27</code></pre>
<pre class="r"><code>budget.bad.lc5.councillor.comp.complete$partyID=ifelse(budget.bad.lc5.councillor.comp.complete$partyID==&quot;upc&quot;, &quot;other&quot;, budget.bad.lc5.councillor.comp.complete$partyID)
budget.bad.lc5.councillor.comp.complete$partyID=ifelse(budget.bad.lc5.councillor.comp.complete$partyID==&quot;ufa&quot;, &quot;other&quot;, budget.bad.lc5.councillor.comp.complete$partyID)
budget.bad.lc5.councillor.comp.complete$partyID=ifelse(budget.bad.lc5.councillor.comp.complete$partyID==&quot;other-noincumbentpartymatch&quot;, &quot;other&quot;, budget.bad.lc5.councillor.comp.complete$partyID)
xtabs(~budget.bad.lc5.councillor.comp.complete$partyID)</code></pre>
<pre><code>## budget.bad.lc5.councillor.comp.complete$partyID
##          dp         fdc independent         nrm       other 
##          38         192         295        2170          23</code></pre>
<pre class="r"><code>t1.P=prop.table(sort(table(budget.good.lc5.chair.comp.complete$partyID), decreasing=TRUE))
t1.N=sort(table(budget.good.lc5.chair.comp.complete$partyID), decreasing=TRUE)

t2.P=prop.table(sort(table(budget.bad.lc5.chair.comp.complete$partyID), decreasing=TRUE))
t2.N=sort(table(budget.bad.lc5.chair.comp.complete$partyID), decreasing=TRUE)

t3.P=prop.table(sort(table(budget.good.lc5.councillor.comp.complete$partyID), decreasing=TRUE))
t3.N=sort(table(budget.good.lc5.councillor.comp.complete$partyID), decreasing=TRUE)

t4.P=prop.table(sort(table(budget.bad.lc5.councillor.comp.complete$partyID), decreasing=TRUE))
t4.N=sort(table(budget.bad.lc5.councillor.comp.complete$partyID), decreasing=TRUE)

##Incumbent Party ID
t5.P=prop.table(sort(table(budget.good.lc5.chair.comp.complete$lc5.chair.party), decreasing=TRUE))
t5.N=(sort(table(budget.good.lc5.chair.comp.complete$lc5.chair.party), decreasing=TRUE))

t6.P=prop.table(sort(table(budget.bad.lc5.chair.comp.complete$lc5.chair.party), decreasing=TRUE))
t6.N=(sort(table(budget.bad.lc5.chair.comp.complete$lc5.chair.party), decreasing=TRUE))

t7.P=prop.table(sort(table(budget.good.lc5.councillor.comp.complete$lc5.councillor.party), decreasing=TRUE))
t7.N=(sort(table(budget.good.lc5.councillor.comp.complete$lc5.councillor.party), decreasing=TRUE))

t8.P=prop.table(sort(table(budget.bad.lc5.councillor.comp.complete$lc5.councillor.party), decreasing=TRUE))
t8.N=(sort(table(budget.bad.lc5.councillor.comp.complete$lc5.councillor.party), decreasing=TRUE))

##Voter-Incumbent Alignment
t9.P=prop.table(sort(table(budget.good.lc5.chair.comp.complete$aligned.lc5.chair.inc), decreasing=TRUE))
t9.N=(sort(table(budget.good.lc5.chair.comp.complete$aligned.lc5.chair.inc), decreasing=TRUE))

t10.P=prop.table(sort(table(budget.bad.lc5.chair.comp.complete$aligned.lc5.chair.inc), decreasing=TRUE))
t10.N=(sort(table(budget.bad.lc5.chair.comp.complete$aligned.lc5.chair.inc), decreasing=TRUE))

t11.P=prop.table(sort(table(budget.good.lc5.chair.comp.complete$aligned.lc5.councillor.inc), decreasing=TRUE))
t11.N=(sort(table(budget.good.lc5.chair.comp.complete$aligned.lc5.councillor.inc), decreasing=TRUE))

t12.P=prop.table(sort(table(budget.bad.lc5.chair.comp.complete$aligned.lc5.councillor.inc), decreasing=TRUE))
t12.N=(sort(table(budget.bad.lc5.chair.comp.complete$aligned.lc5.councillor.inc), decreasing=TRUE))

#totals
t13.N=nrow(budget.good.lc5.chair.comp.complete)
t14.N=nrow(budget.bad.lc5.chair.comp.complete)

t15.N=nrow(budget.good.lc5.councillor.comp.complete)
t16.N=nrow(budget.bad.lc5.councillor.comp.complete)

for(i in 1:8){
  N=eval(parse(text=paste(&quot;data.frame(t&quot;,i,&quot;.N)[,2]&quot;, sep=&quot;&quot;)))
  P=eval(parse(text=paste(&quot;data.frame(t&quot;,i,&quot;.P)[,2]&quot;, sep=&quot;&quot;)))
  S=paste(N, &quot; (&quot;, round(P, 2)*100, &quot;%)&quot;, sep=&quot;&quot;)
  assign(paste(&quot;t&quot;, i, sep=&quot;&quot;), data.frame(S))
}

for(i in 9:12){
  N=eval(parse(text=paste(&quot;data.frame(t&quot;,i,&quot;.N)[1,2]&quot;, sep=&quot;&quot;)))
  P=eval(parse(text=paste(&quot;data.frame(t&quot;,i,&quot;.P)[1,2]&quot;, sep=&quot;&quot;)))
  S=paste(N, &quot; (&quot;, round(P, 2)*100, &quot;%)&quot;, sep=&quot;&quot;)
  assign(paste(&quot;t&quot;, i, sep=&quot;&quot;), data.frame(S))
}

partylabels.councillor=c(&quot;Party ID voter: NRM&quot;, &quot;Party ID voter: Independent&quot;, &quot;Party ID voter: FDC&quot;, &quot;Party ID voter: DP&quot;, &quot;Party ID voter: Other&quot;, &quot;Party ID Incumbent: NRM&quot;, &quot;Party ID Incumbent: Independent&quot;, &quot;Party ID Incumbent: FDC&quot;, &quot;Party ID Incumbent: DP&quot;, &quot;Party ID Incumbent: Other&quot;, &quot;Voter-Incumbent Alignment&quot;, &quot;Total Responses&quot;)
partylabels.chair=c(&quot;Party ID voter: NRM&quot;, &quot;Party ID voter: Independent&quot;, &quot;Party ID voter: FDC&quot;, &quot;Party ID voter: DP&quot;, &quot;Party ID voter: Other&quot;, &quot;Party ID Incumbent: NRM&quot;, &quot;Party ID Incumbent: Independent&quot;, &quot;Party ID Incumbent: FDC&quot;, &quot;Voter-Incumbent Alignment&quot;, &quot;Total Responses&quot;)

headers=c(&quot;Good News&quot;, &quot;Bad News&quot;)

tab.chair=NULL
tab.chair=rbind(data.frame(t1,t2), data.frame(t5,t6))
tab.align=data.frame( t9[1],t10[1])
names(tab.align)=names(tab.chair)
tab.tot=data.frame(toString(t13.N),toString(t14.N))
names(tab.tot)=names(tab.chair)
tab.chair=rbind(tab.chair, tab.align, tab.tot)
names(tab.chair)=headers
row.names(tab.chair)=partylabels.chair

tab.councillor=NULL
tab.councillor=rbind(data.frame(t3,t4), data.frame(t7,t8))
tab.align=data.frame(t11[1],t12[1])
names(tab.align)=names(tab.councillor)
tab.tot=data.frame(toString(t15.N),toString(t16.N))
names(tab.tot)=names(tab.councillor)
tab.councillor=rbind(tab.councillor, tab.align, tab.tot)
names(tab.councillor)=headers
row.names(tab.councillor)=partylabels.councillor

print(xtable(tab.chair))</code></pre>
<pre><code>## % latex table generated in R 3.5.0 by xtable 1.8-2 package
## % Fri Apr 27 11:19:08 2018
## \begin{table}[ht]
## \centering
## \begin{tabular}{rll}
##   \hline
##  &amp; Good News &amp; Bad News \\ 
##   \hline
## Party ID voter: NRM &amp; 2432 (68\%) &amp; 2290 (77\%) \\ 
##   Party ID voter: Independent &amp; 638 (18\%) &amp; 378 (13\%) \\ 
##   Party ID voter: FDC &amp; 360 (10\%) &amp; 219 (7\%) \\ 
##   Party ID voter: DP &amp; 114 (3\%) &amp; 44 (1\%) \\ 
##   Party ID voter: Other &amp; 34 (1\%) &amp; 27 (1\%) \\ 
##   Party ID Incumbent: NRM &amp; 2994 (76\%) &amp; 2338 (75\%) \\ 
##   Party ID Incumbent: Independent &amp; 879 (22\%) &amp; 526 (17\%) \\ 
##   Party ID Incumbent: FDC &amp; 48 (1\%) &amp; 267 (9\%) \\ 
##   Voter-Incumbent Alignment &amp; 2225 (62\%) &amp; 1921 (65\%) \\ 
##   Total Responses &amp; 3921 &amp; 3131 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>print(xtable(tab.councillor))</code></pre>
<pre><code>## % latex table generated in R 3.5.0 by xtable 1.8-2 package
## % Fri Apr 27 11:19:08 2018
## \begin{table}[ht]
## \centering
## \begin{tabular}{rll}
##   \hline
##  &amp; Good News &amp; Bad News \\ 
##   \hline
## Party ID voter: NRM &amp; 1970 (70\%) &amp; 2170 (80\%) \\ 
##   Party ID voter: Independent &amp; 442 (16\%) &amp; 295 (11\%) \\ 
##   Party ID voter: FDC &amp; 292 (10\%) &amp; 192 (7\%) \\ 
##   Party ID voter: DP &amp; 91 (3\%) &amp; 38 (1\%) \\ 
##   Party ID voter: Other &amp; 27 (1\%) &amp; 23 (1\%) \\ 
##   Party ID Incumbent: NRM &amp; 2316 (75\%) &amp; 2230 (77\%) \\ 
##   Party ID Incumbent: Independent &amp; 317 (10\%) &amp; 312 (11\%) \\ 
##   Party ID Incumbent: FDC &amp; 238 (8\%) &amp; 254 (9\%) \\ 
##   Party ID Incumbent: DP &amp; 187 (6\%) &amp; 56 (2\%) \\ 
##   Party ID Incumbent: Other &amp; 16 (1\%) &amp; 31 (1\%) \\ 
##   Voter-Incumbent Alignment &amp; 1760 (55\%) &amp; 1857 (63\%) \\ 
##   Total Responses &amp; 3074 &amp; 2883 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S8: Attrition with respect to the budget treatment
######################################################################################

d.attr.tab &lt;- table(data$budget.treat,data$d.attr)
d.attr.tab</code></pre>
<pre><code>##    
##        0    1
##   0 6237 1782
##   1 6344 1720</code></pre>
<pre class="r"><code>chisq.test(d.attr.tab)</code></pre>
<pre><code>## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  d.attr.tab
## X-squared = 1.8297, df = 1, p-value = 0.1762</code></pre>
<pre class="r"><code>d.attr.prop &lt;- prop.table(d.attr.tab, margin=1)
d.attr.prop</code></pre>
<pre><code>##    
##             0         1
##   0 0.7777778 0.2222222
##   1 0.7867063 0.2132937</code></pre>
<pre class="r"><code>######################################################################################
###Table S9: Attrition with respect to the budget treatment and density
######################################################################################

d.attr.tab &lt;- table(data$bd.multi.treat,data$d.attr)
d.attr.tab</code></pre>
<pre><code>##                   
##                       0    1
##   budget0.density0 4263 1220
##   budget0.density1 1101  285
##   budget1.density0 1077  300
##   budget1.density1 4400 1147</code></pre>
<pre class="r"><code>chisq.test(d.attr.tab)</code></pre>
<pre><code>## 
##  Pearson's Chi-squared test
## 
## data:  d.attr.tab
## X-squared = 4.7777, df = 3, p-value = 0.1888</code></pre>
<pre class="r"><code>d.attr.prop &lt;- prop.table(d.attr.tab, margin=1)
d.attr.prop</code></pre>
<pre><code>##                   
##                            0         1
##   budget0.density0 0.7774941 0.2225059
##   budget0.density1 0.7943723 0.2056277
##   budget1.density0 0.7821351 0.2178649
##   budget1.density1 0.7932216 0.2067784</code></pre>
<pre class="r"><code>######################################################################################
###Table S10: Treatment effects on reported turnout
######################################################################################

h7a.b.1 &lt;- felm(lc5.turnout ~ budget.treat + lc5.turnout.intent | location.id | 0 | district_lower, data=budget.good)
h7b.b.1 &lt;- felm(lc5.turnout ~ budget.treat + lc5.turnout.intent | location.id | 0 | district_lower, data=budget.bad)
h7a.b.1c &lt;- felm(lc5.turnout ~ budget.treat + lc5.turnout.intent + b.Q1_living_conditions + turnout.2011.LC5 + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | district_lower, data=budget.good)
h7b.b.1c &lt;- felm(lc5.turnout ~ budget.treat + lc5.turnout.intent + b.Q1_living_conditions + turnout.2011.LC5 + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | district_lower, data=budget.bad)

lc5.turnout.good &lt;- felm.ri(lc5.turnout ~ budget.treat + lc5.turnout.intent | location.id | 0 | district_lower,
                            dta=budget.good, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                            rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>lc5.turnout.good.cov &lt;- felm.ri(lc5.turnout ~ budget.treat + lc5.turnout.intent + b.Q1_living_conditions + turnout.2011.LC5 + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | district_lower,
                                dta=budget.good, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                                rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>lc5.turnout.bad &lt;- felm.ri(lc5.turnout ~ budget.treat + lc5.turnout.intent | location.id | 0 | district_lower, 
                           dta=budget.bad, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                           rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>lc5.turnout.bad.cov &lt;- felm.ri(lc5.turnout ~ budget.treat + lc5.turnout.intent + b.Q1_living_conditions + turnout.2011.LC5 + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | district_lower, 
                               dta=budget.bad, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                               rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>stargazer(h7a.b.1, h7a.b.1c, h7b.b.1, h7b.b.1c, type = &quot;latex&quot;, 
          dep.var.labels.include = FALSE,
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(2,2),
          keep = c(&quot;budget.treat&quot;,&quot;lc5.turnout.intent&quot;),
          covariate.labels = c(&quot;Budget Treatment (RI)&quot;,&quot;Turnout Intent&quot;),
          add.lines = list(c(&quot;Polling station fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;No&quot;, &quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Turnout for LC5 Election&quot;,
          notes = &quot;Notes: SEs derived from randomization inference for treatment and clustered by politician for all other covariates; one-tailed tests for treatment as pre-registered.&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          se = list(c(lc5.turnout.good$se,as.numeric(h7a.b.1$cse[2])),
                    c(lc5.turnout.good.cov$se,as.numeric(h7a.b.1c$cse[2])),
                    c(lc5.turnout.bad$se,as.numeric(h7b.b.1$cse[2])),
                    c(lc5.turnout.bad$se,as.numeric(h7b.b.1c$cse[2]))
          ),
          p = c(lc5.turnout.good$p.one.way.greater,
                lc5.turnout.good.cov$p.one.way.greater,
                lc5.turnout.bad$p.one.way.lesser,
                lc5.turnout.bad.cov$p.one.way.lesser),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Date and time: Fri, Dec 15, 2017 - 09:41:46</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 12:38:12
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{4}{c}{DV: Turnout for LC5 Election} \\ 
## \cline{2-5} 
##  &amp; \multicolumn{2}{c}{Good News} &amp; \multicolumn{2}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment (RI) &amp; $-$0.002 &amp; $-$0.005 &amp; $-$0.0001 &amp; $-$0.001 \\ 
##   &amp; (0.012) &amp; (0.012) &amp; (0.012) &amp; (0.012) \\ 
##   &amp; p = 0.583 &amp; p = 0.650 &amp; p = 0.499 &amp; p = 0.482 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Turnout Intent &amp; 0.186 &amp; 0.152 &amp; 0.135 &amp; 0.102 \\ 
##   &amp; (0.042) &amp; (0.037) &amp; (0.041) &amp; (0.037) \\ 
##   &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Polling station fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; No &amp; Yes &amp; No &amp; Yes \\ 
## Observations &amp; 6,468 &amp; 6,468 &amp; 5,836 &amp; 5,836 \\ 
## Adjusted R$^{2}$ &amp; 0.075 &amp; 0.096 &amp; 0.132 &amp; 0.142 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{5}{r}{Notes: SEs derived from randomization inference for treatment and clustered by politician for all other covariates; one-tailed tests for treatment as pre-registered.} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S11: Treatment effect on reported vote choice for the incumbent, including non-turnout as non-positive outcome
######################################################################################

h1a.b.1c.wt &lt;- felm(lc5.chair.inc.vote.w.turnout ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
h1a.b.2c.wt &lt;- felm(lc5.councillor.inc.vote.w.turnout ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
h1b.b.1c.wt &lt;- felm(lc5.chair.inc.vote.w.turnout ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
h1b.b.2c.wt &lt;- felm(lc5.councillor.inc.vote.w.turnout ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)

h1a.b.1c.wt.ri &lt;- felm.ri(lc5.chair.inc.vote.w.turnout ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, 
                          dta=budget.good.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                          rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>h1a.b.2c.wt.ri &lt;- felm.ri(lc5.councillor.inc.vote.w.turnout ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, 
                          dta=budget.good.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                          rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>h1b.b.1c.wt.ri &lt;- felm.ri(lc5.chair.inc.vote.w.turnout ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, 
                          dta=budget.bad.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                          rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>h1b.b.2c.wt.ri &lt;- felm.ri(lc5.councillor.inc.vote.w.turnout ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, 
                          dta=budget.bad.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                          rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>#Table for four models with covariates
stargazer(h1a.b.1c.wt, h1a.b.2c.wt, h1b.b.1c.wt, h1b.b.2c.wt, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(2,2),
          keep = c(&quot;budget.treat&quot;,&quot;lc5.chair.intent&quot;,&quot;lc5.councillor.intent&quot;),
          add.lines = list(c(&quot;Polling station fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent Including Turnout&quot;,
          covariate.labels = c(&quot;Budget Treatment (RI)&quot;,&quot;LC5 Chair Intent&quot;,&quot;LC5 Councillor Intent&quot;),
          notes.label = &quot;Notes: SEs derived from randomization inference using exact clustered design; one-tailed tests as pre-registered; contested elections only&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          se = list(c(h1a.b.1c.wt.ri$se,h1a.b.1c.wt$cse[[2]]),
                    c(h1a.b.2c.wt.ri$se,h1a.b.2c.wt$cse[[2]]),
                    c(h1b.b.1c.wt.ri$se,h1b.b.1c.wt$cse[[2]]),
                    c(h1b.b.2c.wt.ri$se,h1b.b.2c.wt$cse[[2]])),
          p = c(h1a.b.1c.wt.ri$p.one.way.greater,
                h1a.b.2c.wt.ri$p.one.way.greater,
                h1b.b.1c.wt.ri$p.one.way.lesser,
                h1b.b.2c.wt.ri$p.one.way.lesser),
          report = c('vcsp')
) # Date and time: Thu, Nov 16, 2017 - 12:43:14</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 13:45:30
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{4}{c}{DV: Vote Choice for the Incumbent Including Turnout} \\ 
## \cline{2-5} 
## \\[-1.8ex] &amp; LC5 Chair &amp; LC5 Councillor &amp; LC5 Chair &amp; LC5 Councillor \\ 
##  &amp; \multicolumn{2}{c}{Good News} &amp; \multicolumn{2}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment (RI) &amp; 0.003 &amp; 0.024 &amp; $-$0.001 &amp; $-$0.035 \\ 
##   &amp; (0.016) &amp; (0.017) &amp; (0.017) &amp; (0.017) \\ 
##   &amp; p = 0.419 &amp; p = 0.084 &amp; p = 0.484 &amp; p = 0.020 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  LC5 Chair Intent &amp; 0.081 &amp;  &amp; 0.031 &amp;  \\ 
##   &amp; (0.028) &amp;  &amp; (0.026) &amp;  \\ 
##   &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  LC5 Councillor Intent &amp;  &amp; 0.069 &amp;  &amp; 0.029 \\ 
##   &amp;  &amp; (0.016) &amp;  &amp; (0.017) \\ 
##   &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Polling station fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Observations &amp; 5,070 &amp; 3,911 &amp; 3,950 &amp; 3,656 \\ 
## Adjusted R$^{2}$ &amp; 0.159 &amp; 0.234 &amp; 0.235 &amp; 0.294 \\ 
## \hline 
## \hline \\[-1.8ex] 
## Notes: SEs derived from randomization inference using exact clustered design; one-tailed tests as pre-registered; contested elections only &amp; \multicolumn{4}{r}{$^{*}$p$&lt;$0.1; $^{**}$p$&lt;$0.05; $^{***}$p$&lt;$0.01} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S12: Conditional effects of budget treatment based on the party identification of the voter
######################################################################################

h1a.b.1c.vp &lt;- felm(lc5.chair.inc.vote ~ budget.treat*partyID.slim + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
h1a.b.2c.vp &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*partyID.slim + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
h1b.b.1c.vp &lt;- felm(lc5.chair.inc.vote ~ budget.treat*partyID.slim + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
h1b.b.2c.vp &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*partyID.slim + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)

stargazer(h1a.b.1c.vp, h1a.b.2c.vp, h1b.b.1c.vp, h1b.b.2c.vp, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(2,2),
          keep = c(&quot;budget.treat&quot;,&quot;partyID.slim&quot;),
          covariate.labels = c(&quot;Budget Treatment&quot;,&quot;Opposition Voter&quot;,&quot;Independent Voter&quot;,&quot;Budget Treatment * Opposition Voter&quot;,&quot;Budget Treatment * Independent Voter&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          notes = &quot;Notes: SEs clustered by politician; two-tailed tests; contested elections only without party switching by incumbent; baseline condition is NRM-aligned (ruling party) voter&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Date and time: Thu, Dec 07, 2017 - 17:32:36</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 13:45:31
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{4}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-5} 
## \\[-1.8ex] &amp; LC5 Chair &amp; LC5 Councillor &amp; LC5 Chair &amp; LC5 Councillor \\ 
##  &amp; \multicolumn{2}{c}{Good News} &amp; \multicolumn{2}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment &amp; 0.0002 &amp; 0.066 &amp; 0.003 &amp; $-$0.043 \\ 
##   &amp; (0.020) &amp; (0.018) &amp; (0.021) &amp; (0.017) \\ 
##   &amp; p = 0.994 &amp; p = 0.0002 &amp; p = 0.898 &amp; p = 0.015 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Opposition Voter &amp; $-$0.129 &amp; $-$0.017 &amp; $-$0.073 &amp; $-$0.058 \\ 
##   &amp; (0.048) &amp; (0.047) &amp; (0.082) &amp; (0.051) \\ 
##   &amp; p = 0.008 &amp; p = 0.715 &amp; p = 0.375 &amp; p = 0.254 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Independent Voter &amp; $-$0.048 &amp; $-$0.050 &amp; $-$0.050 &amp; $-$0.031 \\ 
##   &amp; (0.051) &amp; (0.034) &amp; (0.040) &amp; (0.042) \\ 
##   &amp; p = 0.349 &amp; p = 0.142 &amp; p = 0.217 &amp; p = 0.450 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Budget Treatment * Opposition Voter &amp; 0.047 &amp; $-$0.124 &amp; 0.059 &amp; 0.059 \\ 
##   &amp; (0.082) &amp; (0.050) &amp; (0.072) &amp; (0.055) \\ 
##   &amp; p = 0.566 &amp; p = 0.014 &amp; p = 0.410 &amp; p = 0.287 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Budget Treatment * Independent Voter &amp; $-$0.010 &amp; $-$0.077 &amp; 0.021 &amp; 0.005 \\ 
##   &amp; (0.030) &amp; (0.041) &amp; (0.066) &amp; (0.051) \\ 
##   &amp; p = 0.730 &amp; p = 0.059 &amp; p = 0.757 &amp; p = 0.925 \\ 
##   &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Observations &amp; 3,578 &amp; 2,822 &amp; 2,958 &amp; 2,718 \\ 
## Adjusted R$^{2}$ &amp; 0.223 &amp; 0.333 &amp; 0.352 &amp; 0.401 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{5}{r}{Notes: SEs clustered by politician; two-tailed tests; contested elections only without party switching by incumbent; baseline condition is NRM-aligned (ruling party) voter} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S13: Conditional effects of budget treatment based on the alignment of the voter and the incumbent
######################################################################################

h1a.b.1c.aligned &lt;- felm(lc5.chair.inc.vote ~ budget.treat*aligned.lc5.chair.inc.noNA + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
h1a.b.2c.aligned &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*aligned.lc5.councillor.inc.noNA + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
h1b.b.1c.aligned &lt;- felm(lc5.chair.inc.vote ~ budget.treat*aligned.lc5.chair.inc.noNA + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
h1b.b.2c.aligned &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*aligned.lc5.councillor.inc.noNA + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)

stargazer(h1a.b.1c.aligned, h1a.b.2c.aligned, h1b.b.1c.aligned, h1b.b.2c.aligned, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(2,2),
          keep = c(&quot;budget.treat&quot;,&quot;aligned.lc5.chair.inc.noNA&quot;,&quot;aligned.lc5.councillor.inc.noNA&quot;),
          covariate.labels = c(&quot;Budget Treatment&quot;,&quot;Aligned&quot;,&quot;Alignment Unknown&quot;,&quot;Aligned&quot;,&quot;Alignment Unknown&quot;,&quot;Budget Treatment * Aligned&quot;,&quot;Budget Treatment * Alignment Unknown&quot;,&quot;Budget Treatment * Aligned&quot;,&quot;Budget Treatment * Alignment Unknown&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          notes = &quot;Notes: SEs clustered by politician; two-tailed tests; contested elections only without party switching by incumbent; baseline condition is not aligned incumbent and voter.&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Date and time: Thu, Dec 07, 2017 - 18:14:43</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 13:45:32
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{4}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-5} 
## \\[-1.8ex] &amp; LC5 Chair &amp; LC5 Councillor &amp; LC5 Chair &amp; LC5 Councillor \\ 
##  &amp; \multicolumn{2}{c}{Good News} &amp; \multicolumn{2}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment &amp; 0.006 &amp; $-$0.0005 &amp; 0.029 &amp; $-$0.056 \\ 
##   &amp; (0.032) &amp; (0.024) &amp; (0.037) &amp; (0.030) \\ 
##   &amp; p = 0.863 &amp; p = 0.985 &amp; p = 0.428 &amp; p = 0.063 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Aligned &amp; 0.149 &amp;  &amp; 0.115 &amp;  \\ 
##   &amp; (0.030) &amp;  &amp; (0.041) &amp;  \\ 
##   &amp; p = 0.00000 &amp;  &amp; p = 0.005 &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Alignment Unknown &amp; 0.105 &amp;  &amp; 0.093 &amp;  \\ 
##   &amp; (0.055) &amp;  &amp; (0.053) &amp;  \\ 
##   &amp; p = 0.055 &amp;  &amp; p = 0.081 &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Aligned &amp;  &amp; 0.099 &amp;  &amp; 0.073 \\ 
##   &amp;  &amp; (0.030) &amp;  &amp; (0.034) \\ 
##   &amp;  &amp; p = 0.002 &amp;  &amp; p = 0.031 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Alignment Unknown &amp;  &amp; 0.063 &amp;  &amp; 0.040 \\ 
##   &amp;  &amp; (0.049) &amp;  &amp; (0.049) \\ 
##   &amp;  &amp; p = 0.203 &amp;  &amp; p = 0.412 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Budget Treatment * Aligned &amp; $-$0.006 &amp;  &amp; $-$0.030 &amp;  \\ 
##   &amp; (0.038) &amp;  &amp; (0.048) &amp;  \\ 
##   &amp; p = 0.882 &amp;  &amp; p = 0.530 &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Budget Treatment * Alignment Unknown &amp; $-$0.022 &amp;  &amp; $-$0.022 &amp;  \\ 
##   &amp; (0.061) &amp;  &amp; (0.075) &amp;  \\ 
##   &amp; p = 0.718 &amp;  &amp; p = 0.770 &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Budget Treatment * Aligned &amp;  &amp; 0.060 &amp;  &amp; 0.032 \\ 
##   &amp;  &amp; (0.032) &amp;  &amp; (0.037) \\ 
##   &amp;  &amp; p = 0.059 &amp;  &amp; p = 0.381 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Budget Treatment * Alignment Unknown &amp;  &amp; $-$0.052 &amp;  &amp; 0.006 \\ 
##   &amp;  &amp; (0.061) &amp;  &amp; (0.079) \\ 
##   &amp;  &amp; p = 0.397 &amp;  &amp; p = 0.940 \\ 
##   &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Observations &amp; 3,921 &amp; 3,074 &amp; 3,131 &amp; 2,883 \\ 
## Adjusted R$^{2}$ &amp; 0.221 &amp; 0.335 &amp; 0.350 &amp; 0.400 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{5}{r}{Notes: SEs clustered by politician; two-tailed tests; contested elections only without party switching by incumbent; baseline condition is not aligned incumbent and voter.} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>#Note: have to rearrange table to combine chair / councillor interaction terms


######################################################################################
###Table S14: Results on vote choice for primary specification (main effects)
######################################################################################

#Duplicate of &quot;Main Effects&quot; section in the &quot;Figure 1&quot; block in BJNP_PNAS_replication.R
###Budget / LC5 chair results
chair.good &lt;- felm.ri(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name,
                      dta=budget.good.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                      rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.bad &lt;- felm.ri(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, 
                     dta=budget.bad.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                     rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.good.cov &lt;- felm.ri(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name,
                          dta=budget.good.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                          rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.bad.cov &lt;- felm.ri(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, 
                         dta=budget.bad.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                         rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>###Budget / LC5 councillor results
coun.good &lt;- felm.ri(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name,
                     dta=budget.good.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                     rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.bad &lt;- felm.ri(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, 
                    dta=budget.bad.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                    rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.good.cov &lt;- felm.ri(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name,
                         dta=budget.good.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                         rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.bad.cov &lt;- felm.ri(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, 
                        dta=budget.bad.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                        rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>h1a.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
h1a.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
h1b.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
h1b.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)
h1a.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
h1a.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
h1b.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
h1b.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)

stargazer(h1a.b.1, h1a.b.1c, h1a.b.2, h1a.b.2c, h1b.b.1, h1b.b.1c, h1b.b.2, h1b.b.2c, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(4,4),
          covariate.labels = c(&quot;Budget Treatment (RI)&quot;,&quot;LC5 Chair Intent&quot;,&quot;Living Conditions: Don't Know&quot;,&quot;Living Conditions: Missing&quot;,&quot;Living Conditions: Much Better&quot;,&quot;Living Conditions: Much Worse&quot;,&quot;Living Conditions: Refused&quot;,&quot;Living Conditions: Same&quot;, &quot;Living Conditions: Worse&quot;, &quot;Trust in Twaweza: Don't Know&quot;, &quot;Trust in Twaweza: Missing&quot;, &quot;Trust in Twaweza: A Little&quot;, &quot;Trust in Twaweza: A Lot&quot;, &quot;Voting Not Secret: Missing&quot;, &quot;Voting Not Secret: Not At All Likely&quot;, &quot;Voting Not Secret: Not Very Likely&quot;, &quot;Voting Not Secret: Refused&quot;, &quot;Voting Not Secret: Somewhat Likely&quot;, &quot;Voting Not Secret: Very Likely&quot;, &quot;Fair Vote Counting: Missing&quot;, &quot;Fair Vote Counting: Not At All Likely&quot;, &quot;Fair Vote Counting: Not Very Likely&quot;, &quot;Fair Vote Counting: Refused&quot;, &quot;Fair Vote Counting: Somewhat Likely&quot;, &quot;Fair Vote Counting: Very Likely&quot;, &quot;Male&quot;, &quot;Education: Some Primary&quot;, &quot;Education: Completed Primary&quot;, &quot;Education: Some Secondary&quot;, &quot;Education: Completed Secondary&quot;, &quot;Education: Some University&quot;, &quot;Education: Completed University&quot;, &quot;Education: Some Post-Graduate&quot;, &quot;Education: Completed Masters&quot;, &quot;Education: Refused&quot;, &quot;Age&quot;, &quot;LC5 Councillor Intent&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          notes = &quot;Notes: SEs derived from randomization inference for treatment and clustered by politician for all other covariates; one-tailed tests on treatment as pre-registered; contested elections where incumbent did not switch parties only.&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          se = list(c(chair.good$se,as.numeric(h1a.b.1$cse[2:length(h1a.b.1$cse)])),
                    c(chair.good.cov$se,as.numeric(h1a.b.1c$cse[2:length(h1a.b.1c$cse)])),
                    c(coun.good$se,as.numeric(h1a.b.2$cse[2:length(h1a.b.2$cse)])),
                    c(coun.good.cov$se,as.numeric(h1a.b.2c$cse[2:length(h1a.b.2c$cse)])),
                    c(chair.bad$se,as.numeric(h1b.b.1$cse[2:length(h1b.b.1$cse)])),
                    c(chair.bad.cov$se,as.numeric(h1b.b.1c$cse[2:length(h1b.b.1c$cse)])),
                    c(coun.bad$se,as.numeric(h1b.b.2$cse[2:length(h1b.b.2$cse)])),
                    c(coun.bad.cov$se,as.numeric(h1b.b.2c$cse[2:length(h1b.b.2c$cse)]))
          ),
          p = c(chair.good$p.one.way.greater,
                chair.good.cov$p.one.way.greater,
                coun.good$p.one.way.greater,
                coun.good.cov$p.one.way.greater,
                chair.bad$p.one.way.lesser,
                chair.bad.cov$p.one.way.lesser,
                coun.bad$p.one.way.lesser,
                coun.bad.cov$p.one.way.lesser),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Sun, Mar 18, 2018 - 08:58:59</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 15:46:38
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{8}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-9} 
## \\[-1.8ex] &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} \\ 
##  &amp; \multicolumn{4}{c}{Good News} &amp; \multicolumn{4}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4) &amp; (5) &amp; (6) &amp; (7) &amp; (8)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment (RI) &amp; 0.002 &amp; 0.002 &amp; 0.027 &amp; 0.028 &amp; 0.011 &amp; 0.009 &amp; $-$0.034 &amp; $-$0.035 \\ 
##   &amp; (0.017) &amp; (0.017) &amp; (0.018) &amp; (0.018) &amp; (0.016) &amp; (0.016) &amp; (0.016) &amp; (0.016) \\ 
##   &amp; p = 0.454 &amp; p = 0.451 &amp; p = 0.065 &amp; p = 0.061 &amp; p = 0.754 &amp; p = 0.722 &amp; p = 0.015 &amp; p = 0.013 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Chair Intent &amp; 0.081 &amp; 0.074 &amp;  &amp;  &amp; 0.036 &amp; 0.032 &amp;  &amp;  \\ 
##   &amp; (0.038) &amp; (0.033) &amp;  &amp;  &amp; (0.024) &amp; (0.023) &amp;  &amp;  \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Don't Know &amp;  &amp; 0.100 &amp;  &amp; 0.067 &amp;  &amp; 0.018 &amp;  &amp; 0.007 \\ 
##   &amp;  &amp; (0.089) &amp;  &amp; (0.132) &amp;  &amp; (0.053) &amp;  &amp; (0.059) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Missing &amp;  &amp; $-$0.160 &amp;  &amp; 0.014 &amp;  &amp; $-$0.130 &amp;  &amp; 0.016 \\ 
##   &amp;  &amp; (0.235) &amp;  &amp; (0.172) &amp;  &amp; (0.219) &amp;  &amp; (0.130) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Much Better &amp;  &amp; 0.094 &amp;  &amp; 0.091 &amp;  &amp; $-$0.051 &amp;  &amp; 0.023 \\ 
##   &amp;  &amp; (0.025) &amp;  &amp; (0.044) &amp;  &amp; (0.040) &amp;  &amp; (0.043) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Much Worse &amp;  &amp; $-$0.031 &amp;  &amp; 0.005 &amp;  &amp; 0.001 &amp;  &amp; $-$0.009 \\ 
##   &amp;  &amp; (0.026) &amp;  &amp; (0.040) &amp;  &amp; (0.033) &amp;  &amp; (0.026) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Refused &amp;  &amp; $-$0.112 &amp;  &amp; 0.272 &amp;  &amp; 0.328 &amp;  &amp; $-$0.033 \\ 
##   &amp;  &amp; (0.200) &amp;  &amp; (0.388) &amp;  &amp; (0.111) &amp;  &amp; (0.156) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Same &amp;  &amp; 0.002 &amp;  &amp; $-$0.031 &amp;  &amp; $-$0.012 &amp;  &amp; $-$0.021 \\ 
##   &amp;  &amp; (0.018) &amp;  &amp; (0.025) &amp;  &amp; (0.022) &amp;  &amp; (0.024) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Worse &amp;  &amp; $-$0.009 &amp;  &amp; $-$0.019 &amp;  &amp; $-$0.029 &amp;  &amp; 0.015 \\ 
##   &amp;  &amp; (0.023) &amp;  &amp; (0.021) &amp;  &amp; (0.012) &amp;  &amp; (0.018) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Trust in Twaweza: Don't Know &amp;  &amp; $-$0.021 &amp;  &amp; $-$0.026 &amp;  &amp; 0.088 &amp;  &amp; $-$0.003 \\ 
##   &amp;  &amp; (0.053) &amp;  &amp; (0.062) &amp;  &amp; (0.055) &amp;  &amp; (0.051) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Trust in Twaweza: Missing &amp;  &amp; $-$0.190 &amp;  &amp; $-$0.029 &amp;  &amp; $-$0.004 &amp;  &amp; $-$0.003 \\ 
##   &amp;  &amp; (0.198) &amp;  &amp; (0.118) &amp;  &amp; (0.116) &amp;  &amp; (0.070) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Trust in Twaweza: A Little &amp;  &amp; $-$0.021 &amp;  &amp; 0.013 &amp;  &amp; 0.083 &amp;  &amp; $-$0.008 \\ 
##   &amp;  &amp; (0.039) &amp;  &amp; (0.055) &amp;  &amp; (0.046) &amp;  &amp; (0.037) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Trust in Twaweza: A Lot &amp;  &amp; $-$0.004 &amp;  &amp; $-$0.001 &amp;  &amp; 0.075 &amp;  &amp; $-$0.021 \\ 
##   &amp;  &amp; (0.039) &amp;  &amp; (0.060) &amp;  &amp; (0.040) &amp;  &amp; (0.042) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Missing &amp;  &amp; 0.176 &amp;  &amp; $-$0.157 &amp;  &amp; 0.011 &amp;  &amp; $-$0.127 \\ 
##   &amp;  &amp; (0.279) &amp;  &amp; (0.158) &amp;  &amp; (0.033) &amp;  &amp; (0.042) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Not At All Likely &amp;  &amp; $-$0.002 &amp;  &amp; $-$0.025 &amp;  &amp; $-$0.031 &amp;  &amp; $-$0.054 \\ 
##   &amp;  &amp; (0.033) &amp;  &amp; (0.039) &amp;  &amp; (0.026) &amp;  &amp; (0.032) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Not Very Likely &amp;  &amp; 0.011 &amp;  &amp; 0.001 &amp;  &amp; $-$0.012 &amp;  &amp; $-$0.059 \\ 
##   &amp;  &amp; (0.049) &amp;  &amp; (0.051) &amp;  &amp; (0.027) &amp;  &amp; (0.029) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Refused &amp;  &amp; 0.087 &amp;  &amp; $-$0.108 &amp;  &amp; $-$0.086 &amp;  &amp; 0.101 \\ 
##   &amp;  &amp; (0.194) &amp;  &amp; (0.132) &amp;  &amp; (0.065) &amp;  &amp; (0.058) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Somewhat Likely &amp;  &amp; $-$0.032 &amp;  &amp; $-$0.062 &amp;  &amp; $-$0.067 &amp;  &amp; $-$0.063 \\ 
##   &amp;  &amp; (0.032) &amp;  &amp; (0.050) &amp;  &amp; (0.027) &amp;  &amp; (0.032) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Very Likely &amp;  &amp; $-$0.002 &amp;  &amp; $-$0.014 &amp;  &amp; $-$0.011 &amp;  &amp; $-$0.027 \\ 
##   &amp;  &amp; (0.052) &amp;  &amp; (0.043) &amp;  &amp; (0.033) &amp;  &amp; (0.033) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Missing &amp;  &amp; $-$0.192 &amp;  &amp; $-$0.072 &amp;  &amp; $-$0.111 &amp;  &amp; $-$0.363 \\ 
##   &amp;  &amp; (0.204) &amp;  &amp; (0.238) &amp;  &amp; (0.118) &amp;  &amp; (0.182) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Not At All Likely &amp;  &amp; $-$0.061 &amp;  &amp; $-$0.044 &amp;  &amp; $-$0.034 &amp;  &amp; 0.039 \\ 
##   &amp;  &amp; (0.025) &amp;  &amp; (0.040) &amp;  &amp; (0.043) &amp;  &amp; (0.037) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Not Very Likely &amp;  &amp; $-$0.037 &amp;  &amp; $-$0.068 &amp;  &amp; $-$0.037 &amp;  &amp; $-$0.018 \\ 
##   &amp;  &amp; (0.024) &amp;  &amp; (0.040) &amp;  &amp; (0.039) &amp;  &amp; (0.032) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Refused &amp;  &amp; 0.037 &amp;  &amp; $-$0.002 &amp;  &amp; $-$0.017 &amp;  &amp; 0.092 \\ 
##   &amp;  &amp; (0.076) &amp;  &amp; (0.081) &amp;  &amp; (0.094) &amp;  &amp; (0.077) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Somewhat Likely &amp;  &amp; $-$0.031 &amp;  &amp; $-$0.065 &amp;  &amp; 0.031 &amp;  &amp; 0.009 \\ 
##   &amp;  &amp; (0.016) &amp;  &amp; (0.040) &amp;  &amp; (0.032) &amp;  &amp; (0.027) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Very Likely &amp;  &amp; $-$0.026 &amp;  &amp; 0.009 &amp;  &amp; 0.028 &amp;  &amp; 0.002 \\ 
##   &amp;  &amp; (0.032) &amp;  &amp; (0.038) &amp;  &amp; (0.026) &amp;  &amp; (0.028) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Male &amp;  &amp; $-$0.055 &amp;  &amp; $-$0.059 &amp;  &amp; $-$0.034 &amp;  &amp; $-$0.041 \\ 
##   &amp;  &amp; (0.033) &amp;  &amp; (0.020) &amp;  &amp; (0.018) &amp;  &amp; (0.021) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Some Primary &amp;  &amp; $-$0.051 &amp;  &amp; 0.001 &amp;  &amp; $-$0.009 &amp;  &amp; $-$0.090 \\ 
##   &amp;  &amp; (0.031) &amp;  &amp; (0.042) &amp;  &amp; (0.051) &amp;  &amp; (0.030) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Completed Primary &amp;  &amp; $-$0.035 &amp;  &amp; $-$0.034 &amp;  &amp; 0.007 &amp;  &amp; $-$0.034 \\ 
##   &amp;  &amp; (0.037) &amp;  &amp; (0.039) &amp;  &amp; (0.054) &amp;  &amp; (0.030) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Some Secondary &amp;  &amp; $-$0.081 &amp;  &amp; $-$0.052 &amp;  &amp; $-$0.008 &amp;  &amp; $-$0.074 \\ 
##   &amp;  &amp; (0.031) &amp;  &amp; (0.040) &amp;  &amp; (0.049) &amp;  &amp; (0.032) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Completed Secondary &amp;  &amp; $-$0.042 &amp;  &amp; $-$0.027 &amp;  &amp; $-$0.014 &amp;  &amp; $-$0.064 \\ 
##   &amp;  &amp; (0.046) &amp;  &amp; (0.054) &amp;  &amp; (0.057) &amp;  &amp; (0.041) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Some University &amp;  &amp; $-$0.126 &amp;  &amp; $-$0.078 &amp;  &amp; $-$0.070 &amp;  &amp; $-$0.130 \\ 
##   &amp;  &amp; (0.062) &amp;  &amp; (0.061) &amp;  &amp; (0.063) &amp;  &amp; (0.055) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Completed University &amp;  &amp; $-$0.087 &amp;  &amp; $-$0.061 &amp;  &amp; 0.006 &amp;  &amp; $-$0.194 \\ 
##   &amp;  &amp; (0.088) &amp;  &amp; (0.070) &amp;  &amp; (0.061) &amp;  &amp; (0.072) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Some Post-Graduate &amp;  &amp; $-$0.057 &amp;  &amp; $-$0.136 &amp;  &amp; 0.096 &amp;  &amp; 0.057 \\ 
##   &amp;  &amp; (0.096) &amp;  &amp; (0.061) &amp;  &amp; (0.198) &amp;  &amp; (0.077) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Completed Masters &amp;  &amp; $-$0.252 &amp;  &amp; $-$0.210 &amp;  &amp; 0.192 &amp;  &amp; 0.016 \\ 
##   &amp;  &amp; (0.050) &amp;  &amp; (0.040) &amp;  &amp; (0.148) &amp;  &amp; (0.044) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Refused &amp;  &amp; $-$0.755 &amp;  &amp;  &amp;  &amp; 0.176 &amp;  &amp; 0.362 \\ 
##   &amp;  &amp; (0.044) &amp;  &amp;  &amp;  &amp; (0.044) &amp;  &amp; (0.046) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Age &amp;  &amp; 0.002 &amp;  &amp; 0.001 &amp;  &amp; 0.001 &amp;  &amp; 0.001 \\ 
##   &amp;  &amp; (0.001) &amp;  &amp; (0.001) &amp;  &amp; (0.001) &amp;  &amp; (0.001) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Councillor Intent &amp;  &amp;  &amp; 0.060 &amp; 0.052 &amp;  &amp;  &amp; 0.001 &amp; 0.004 \\ 
##   &amp;  &amp;  &amp; (0.016) &amp; (0.016) &amp;  &amp;  &amp; (0.017) &amp; (0.016) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Observations &amp; 3,921 &amp; 3,921 &amp; 3,074 &amp; 3,074 &amp; 3,131 &amp; 3,131 &amp; 2,883 &amp; 2,883 \\ 
## Adjusted R$^{2}$ &amp; 0.199 &amp; 0.205 &amp; 0.317 &amp; 0.323 &amp; 0.341 &amp; 0.345 &amp; 0.392 &amp; 0.396 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{9}{r}{Notes: SEs derived from randomization inference for treatment and clustered by politician for all other covariates; one-tailed tests on treatment as pre-registered; contested elections where incumbent did not switch parties only.} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S15: Results on vote choice for primary specification (verified recipient effects)
######################################################################################

#Duplicate of &quot;Verified Recipient Effects&quot; section in the &quot;Figure 1&quot; block in BJNP_PNAS_replication.R
###Budget / LC5 chair results, verified compliers
chair.good.c &lt;- felm.ri(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name,
                        dta=budget.good.lc5.chair.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                        rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.bad.c &lt;- felm.ri(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, 
                       dta=budget.bad.lc5.chair.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                       rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.good.cov.c &lt;- felm.ri(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name,
                            dta=budget.good.lc5.chair.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                            rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.bad.cov.c &lt;- felm.ri(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, 
                           dta=budget.bad.lc5.chair.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                           rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>###Budget / LC5 councillor results, verified compliers
coun.good.c &lt;- felm.ri(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name,
                       dta=budget.good.lc5.councillor.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                       rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.bad.c &lt;- felm.ri(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, 
                      dta=budget.bad.lc5.councillor.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                      rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.good.cov.c &lt;- felm.ri(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name,
                           dta=budget.good.lc5.councillor.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                           rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.bad.cov.c &lt;- felm.ri(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, 
                          dta=budget.bad.lc5.councillor.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                          rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>h1a.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp.c)
h1a.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp.c)
h1b.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp.c)
h1b.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp.c)
h1a.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp.c)
h1a.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp.c)
h1b.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp.c)
h1b.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp.c)

stargazer(h1a.b.1, h1a.b.1c, h1a.b.2, h1a.b.2c, h1b.b.1, h1b.b.1c, h1b.b.2, h1b.b.2c, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(4,4),
          covariate.labels = c(&quot;Budget Treatment (RI)&quot;,&quot;LC5 Chair Intent&quot;,&quot;Living Conditions: Don't Know&quot;,&quot;Living Conditions: Missing&quot;,&quot;Living Conditions: Much Better&quot;,&quot;Living Conditions: Much Worse&quot;,&quot;Living Conditions: Refused&quot;,&quot;Living Conditions: Same&quot;, &quot;Living Conditions: Worse&quot;, &quot;Trust in Twaweza: Don't Know&quot;, &quot;Trust in Twaweza: Missing&quot;, &quot;Trust in Twaweza: A Little&quot;, &quot;Trust in Twaweza: A Lot&quot;, &quot;Voting Not Secret: Missing&quot;, &quot;Voting Not Secret: Not At All Likely&quot;, &quot;Voting Not Secret: Not Very Likely&quot;, &quot;Voting Not Secret: Refused&quot;, &quot;Voting Not Secret: Somewhat Likely&quot;, &quot;Voting Not Secret: Very Likely&quot;, &quot;Fair Vote Counting: Missing&quot;, &quot;Fair Vote Counting: Not At All Likely&quot;, &quot;Fair Vote Counting: Not Very Likely&quot;, &quot;Fair Vote Counting: Refused&quot;, &quot;Fair Vote Counting: Somewhat Likely&quot;, &quot;Fair Vote Counting: Very Likely&quot;, &quot;Male&quot;, &quot;Education: Some Primary&quot;, &quot;Education: Completed Primary&quot;, &quot;Education: Some Secondary&quot;, &quot;Education: Completed Secondary&quot;, &quot;Education: Some University&quot;, &quot;Education: Completed University&quot;, &quot;Education: Some Post-Graduate&quot;, &quot;Education: Completed Masters&quot;, &quot;Education: Refused&quot;, &quot;Age&quot;, &quot;LC5 Councillor Intent&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          notes = &quot;Notes: SEs derived from randomization inference for treatment and clustered by politician for all other covariates; one-tailed tests on treatment as pre-registered; contested elections where incumbent did not switch parties only.&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          se = list(c(chair.good.c$se,as.numeric(h1a.b.1$cse[2:length(h1a.b.1$cse)])),
                    c(chair.good.cov.c$se,as.numeric(h1a.b.1c$cse[2:length(h1a.b.1c$cse)])),
                    c(coun.good.c$se,as.numeric(h1a.b.2$cse[2:length(h1a.b.2$cse)])),
                    c(coun.good.cov.c$se,as.numeric(h1a.b.2c$cse[2:length(h1a.b.2c$cse)])),
                    c(chair.bad.c$se,as.numeric(h1b.b.1$cse[2:length(h1b.b.1$cse)])),
                    c(chair.bad.cov.c$se,as.numeric(h1b.b.1c$cse[2:length(h1b.b.1c$cse)])),
                    c(coun.bad.c$se,as.numeric(h1b.b.2$cse[2:length(h1b.b.2$cse)])),
                    c(coun.bad.cov.c$se,as.numeric(h1b.b.2c$cse[2:length(h1b.b.2c$cse)]))
          ),
          p = c(chair.good.c$p.one.way.greater,
                chair.good.cov.c$p.one.way.greater,
                coun.good.c$p.one.way.greater,
                coun.good.cov.c$p.one.way.greater,
                chair.bad.c$p.one.way.lesser,
                chair.bad.cov.c$p.one.way.lesser,
                coun.bad.c$p.one.way.lesser,
                coun.bad.cov.c$p.one.way.lesser),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Date and time: Sun, Mar 18, 2018 - 11:13:02</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 17:42:01
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{8}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-9} 
## \\[-1.8ex] &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} \\ 
##  &amp; \multicolumn{4}{c}{Good News} &amp; \multicolumn{4}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4) &amp; (5) &amp; (6) &amp; (7) &amp; (8)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment (RI) &amp; 0.004 &amp; 0.006 &amp; 0.048 &amp; 0.051 &amp; $-$0.007 &amp; $-$0.009 &amp; $-$0.055 &amp; $-$0.056 \\ 
##   &amp; (0.022) &amp; (0.022) &amp; (0.023) &amp; (0.024) &amp; (0.019) &amp; (0.019) &amp; (0.020) &amp; (0.020) \\ 
##   &amp; p = 0.424 &amp; p = 0.391 &amp; p = 0.021 &amp; p = 0.017 &amp; p = 0.349 &amp; p = 0.313 &amp; p = 0.003 &amp; p = 0.003 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Chair Intent &amp; 0.092 &amp; 0.086 &amp;  &amp;  &amp; 0.033 &amp; 0.031 &amp;  &amp;  \\ 
##   &amp; (0.045) &amp; (0.037) &amp;  &amp;  &amp; (0.023) &amp; (0.023) &amp;  &amp;  \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Don't Know &amp;  &amp; $-$0.069 &amp;  &amp; $-$0.006 &amp;  &amp; $-$0.061 &amp;  &amp; 0.018 \\ 
##   &amp;  &amp; (0.123) &amp;  &amp; (0.241) &amp;  &amp; (0.072) &amp;  &amp; (0.073) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Missing &amp;  &amp; $-$0.284 &amp;  &amp; $-$0.023 &amp;  &amp; $-$0.174 &amp;  &amp; 0.035 \\ 
##   &amp;  &amp; (0.218) &amp;  &amp; (0.201) &amp;  &amp; (0.194) &amp;  &amp; (0.063) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Much Better &amp;  &amp; 0.038 &amp;  &amp; 0.071 &amp;  &amp; $-$0.061 &amp;  &amp; 0.023 \\ 
##   &amp;  &amp; (0.077) &amp;  &amp; (0.068) &amp;  &amp; (0.056) &amp;  &amp; (0.046) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Much Worse &amp;  &amp; $-$0.022 &amp;  &amp; 0.003 &amp;  &amp; 0.025 &amp;  &amp; $-$0.011 \\ 
##   &amp;  &amp; (0.039) &amp;  &amp; (0.055) &amp;  &amp; (0.034) &amp;  &amp; (0.032) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Refused &amp;  &amp; $-$0.142 &amp;  &amp; 0.312 &amp;  &amp;  &amp;  &amp; 0.070 \\ 
##   &amp;  &amp; (0.222) &amp;  &amp; (0.498) &amp;  &amp;  &amp;  &amp; (0.036) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Same &amp;  &amp; $-$0.016 &amp;  &amp; $-$0.031 &amp;  &amp; $-$0.035 &amp;  &amp; $-$0.038 \\ 
##   &amp;  &amp; (0.031) &amp;  &amp; (0.033) &amp;  &amp; (0.025) &amp;  &amp; (0.026) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Living Conditions: Worse &amp;  &amp; $-$0.014 &amp;  &amp; $-$0.019 &amp;  &amp; $-$0.037 &amp;  &amp; 0.007 \\ 
##   &amp;  &amp; (0.014) &amp;  &amp; (0.027) &amp;  &amp; (0.018) &amp;  &amp; (0.026) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Trust in Twaweza: Don't Know &amp;  &amp; $-$0.009 &amp;  &amp; 0.042 &amp;  &amp; 0.068 &amp;  &amp; $-$0.014 \\ 
##   &amp;  &amp; (0.081) &amp;  &amp; (0.088) &amp;  &amp; (0.062) &amp;  &amp; (0.046) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Trust in Twaweza: Missing &amp;  &amp; $-$0.218 &amp;  &amp; $-$0.017 &amp;  &amp; $-$0.145 &amp;  &amp; 0.022 \\ 
##   &amp;  &amp; (0.208) &amp;  &amp; (0.167) &amp;  &amp; (0.174) &amp;  &amp; (0.069) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Trust in Twaweza: A Little &amp;  &amp; $-$0.024 &amp;  &amp; 0.046 &amp;  &amp; 0.072 &amp;  &amp; $-$0.020 \\ 
##   &amp;  &amp; (0.062) &amp;  &amp; (0.084) &amp;  &amp; (0.052) &amp;  &amp; (0.040) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Trust in Twaweza: A Lot &amp;  &amp; $-$0.021 &amp;  &amp; 0.061 &amp;  &amp; 0.052 &amp;  &amp; $-$0.016 \\ 
##   &amp;  &amp; (0.066) &amp;  &amp; (0.092) &amp;  &amp; (0.048) &amp;  &amp; (0.042) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Missing &amp;  &amp; 0.468 &amp;  &amp; $-$0.333 &amp;  &amp; $-$0.065 &amp;  &amp; $-$0.057 \\ 
##   &amp;  &amp; (0.049) &amp;  &amp; (0.059) &amp;  &amp; (0.048) &amp;  &amp; (0.071) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Not At All Likely &amp;  &amp; $-$0.011 &amp;  &amp; $-$0.055 &amp;  &amp; $-$0.046 &amp;  &amp; $-$0.060 \\ 
##   &amp;  &amp; (0.033) &amp;  &amp; (0.053) &amp;  &amp; (0.038) &amp;  &amp; (0.037) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Not Very Likely &amp;  &amp; 0.011 &amp;  &amp; $-$0.022 &amp;  &amp; $-$0.021 &amp;  &amp; $-$0.072 \\ 
##   &amp;  &amp; (0.042) &amp;  &amp; (0.065) &amp;  &amp; (0.034) &amp;  &amp; (0.034) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Refused &amp;  &amp; $-$0.014 &amp;  &amp; $-$0.221 &amp;  &amp; $-$0.166 &amp;  &amp; 0.133 \\ 
##   &amp;  &amp; (0.216) &amp;  &amp; (0.174) &amp;  &amp; (0.097) &amp;  &amp; (0.088) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Somewhat Likely &amp;  &amp; $-$0.035 &amp;  &amp; $-$0.053 &amp;  &amp; $-$0.087 &amp;  &amp; $-$0.072 \\ 
##   &amp;  &amp; (0.052) &amp;  &amp; (0.076) &amp;  &amp; (0.036) &amp;  &amp; (0.040) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Voting Not Secret: Very Likely &amp;  &amp; 0.020 &amp;  &amp; 0.018 &amp;  &amp; $-$0.043 &amp;  &amp; $-$0.008 \\ 
##   &amp;  &amp; (0.054) &amp;  &amp; (0.058) &amp;  &amp; (0.042) &amp;  &amp; (0.038) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Missing &amp;  &amp; $-$0.260 &amp;  &amp;  &amp;  &amp; $-$0.074 &amp;  &amp; $-$0.280 \\ 
##   &amp;  &amp; (0.034) &amp;  &amp;  &amp;  &amp; (0.120) &amp;  &amp; (0.152) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Not At All Likely &amp;  &amp; $-$0.066 &amp;  &amp; $-$0.027 &amp;  &amp; $-$0.015 &amp;  &amp; 0.045 \\ 
##   &amp;  &amp; (0.032) &amp;  &amp; (0.058) &amp;  &amp; (0.046) &amp;  &amp; (0.046) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Not Very Likely &amp;  &amp; $-$0.075 &amp;  &amp; $-$0.080 &amp;  &amp; $-$0.080 &amp;  &amp; $-$0.025 \\ 
##   &amp;  &amp; (0.031) &amp;  &amp; (0.054) &amp;  &amp; (0.054) &amp;  &amp; (0.045) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Refused &amp;  &amp; 0.026 &amp;  &amp; $-$0.039 &amp;  &amp; $-$0.007 &amp;  &amp; 0.091 \\ 
##   &amp;  &amp; (0.078) &amp;  &amp; (0.104) &amp;  &amp; (0.132) &amp;  &amp; (0.101) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Somewhat Likely &amp;  &amp; $-$0.044 &amp;  &amp; $-$0.055 &amp;  &amp; 0.003 &amp;  &amp; $-$0.002 \\ 
##   &amp;  &amp; (0.019) &amp;  &amp; (0.058) &amp;  &amp; (0.039) &amp;  &amp; (0.041) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Fair Vote Counting: Very Likely &amp;  &amp; $-$0.043 &amp;  &amp; 0.005 &amp;  &amp; 0.005 &amp;  &amp; 0.001 \\ 
##   &amp;  &amp; (0.038) &amp;  &amp; (0.058) &amp;  &amp; (0.036) &amp;  &amp; (0.046) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Male &amp;  &amp; $-$0.051 &amp;  &amp; $-$0.043 &amp;  &amp; $-$0.016 &amp;  &amp; $-$0.038 \\ 
##   &amp;  &amp; (0.030) &amp;  &amp; (0.025) &amp;  &amp; (0.017) &amp;  &amp; (0.024) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Some Primary &amp;  &amp; $-$0.080 &amp;  &amp; $-$0.025 &amp;  &amp; 0.039 &amp;  &amp; $-$0.022 \\ 
##   &amp;  &amp; (0.043) &amp;  &amp; (0.056) &amp;  &amp; (0.073) &amp;  &amp; (0.052) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Completed Primary &amp;  &amp; $-$0.053 &amp;  &amp; $-$0.064 &amp;  &amp; 0.037 &amp;  &amp; 0.010 \\ 
##   &amp;  &amp; (0.047) &amp;  &amp; (0.056) &amp;  &amp; (0.072) &amp;  &amp; (0.053) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Some Secondary &amp;  &amp; $-$0.116 &amp;  &amp; $-$0.083 &amp;  &amp; 0.016 &amp;  &amp; $-$0.029 \\ 
##   &amp;  &amp; (0.039) &amp;  &amp; (0.059) &amp;  &amp; (0.070) &amp;  &amp; (0.053) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Completed Secondary &amp;  &amp; $-$0.066 &amp;  &amp; $-$0.054 &amp;  &amp; 0.005 &amp;  &amp; 0.015 \\ 
##   &amp;  &amp; (0.071) &amp;  &amp; (0.077) &amp;  &amp; (0.067) &amp;  &amp; (0.056) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Some University &amp;  &amp; $-$0.115 &amp;  &amp; $-$0.164 &amp;  &amp; $-$0.043 &amp;  &amp; $-$0.076 \\ 
##   &amp;  &amp; (0.073) &amp;  &amp; (0.078) &amp;  &amp; (0.093) &amp;  &amp; (0.084) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Completed University &amp;  &amp; $-$0.090 &amp;  &amp; $-$0.085 &amp;  &amp; 0.041 &amp;  &amp; $-$0.144 \\ 
##   &amp;  &amp; (0.115) &amp;  &amp; (0.086) &amp;  &amp; (0.079) &amp;  &amp; (0.088) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Some Post-Graduate &amp;  &amp; $-$0.245 &amp;  &amp; $-$0.281 &amp;  &amp; 0.052 &amp;  &amp; 0.053 \\ 
##   &amp;  &amp; (0.186) &amp;  &amp; (0.092) &amp;  &amp; (0.304) &amp;  &amp; (0.126) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Completed Masters &amp;  &amp; $-$0.249 &amp;  &amp; $-$0.302 &amp;  &amp; $-$0.0002 &amp;  &amp; 0.062 \\ 
##   &amp;  &amp; (0.043) &amp;  &amp; (0.062) &amp;  &amp; (0.065) &amp;  &amp; (0.079) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Education: Refused &amp;  &amp; $-$0.806 &amp;  &amp;  &amp;  &amp; 0.216 &amp;  &amp; 0.422 \\ 
##   &amp;  &amp; (0.055) &amp;  &amp;  &amp;  &amp; (0.070) &amp;  &amp; (0.067) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Age &amp;  &amp; 0.002 &amp;  &amp; 0.001 &amp;  &amp; 0.002 &amp;  &amp; 0.001 \\ 
##   &amp;  &amp; (0.001) &amp;  &amp; (0.001) &amp;  &amp; (0.001) &amp;  &amp; (0.001) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Councillor Intent &amp;  &amp;  &amp; 0.076 &amp; 0.066 &amp;  &amp;  &amp; $-$0.006 &amp; $-$0.003 \\ 
##   &amp;  &amp;  &amp; (0.023) &amp; (0.023) &amp;  &amp;  &amp; (0.021) &amp; (0.021) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Observations &amp; 2,476 &amp; 2,476 &amp; 1,925 &amp; 1,925 &amp; 2,205 &amp; 2,205 &amp; 2,018 &amp; 2,018 \\ 
## Adjusted R$^{2}$ &amp; 0.201 &amp; 0.206 &amp; 0.317 &amp; 0.320 &amp; 0.327 &amp; 0.331 &amp; 0.416 &amp; 0.417 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{9}{r}{Notes: SEs derived from randomization inference for treatment and clustered by politician for all other covariates; one-tailed tests on treatment as pre-registered; contested elections where incumbent did not switch parties only.} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S16: Effect of budget treatment on votes for district (LC V) incumbents, estimated as pre-specified
######################################################################################

h1a.b.1 &lt;- felm(lc5.chair.transformed ~ budget.treat | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
h1a.b.2 &lt;- felm(lc5.councillor.transformed ~ budget.treat | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
h1b.b.1 &lt;- felm(lc5.chair.transformed ~ budget.treat | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
h1b.b.2 &lt;- felm(lc5.councillor.transformed ~ budget.treat | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)
h1a.b.1c &lt;- felm(lc5.chair.transformed ~ budget.treat + b.Q1_living_conditions + b.Q6_vote_for_current_LC5chair + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
h1a.b.2c &lt;- felm(lc5.councillor.transformed ~ budget.treat + b.Q1_living_conditions + b.Q5_vote_for_current_LC5council + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
h1b.b.1c &lt;- felm(lc5.chair.transformed ~ budget.treat + b.Q1_living_conditions + b.Q6_vote_for_current_LC5chair + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
h1b.b.2c &lt;- felm(lc5.councillor.transformed ~ budget.treat + b.Q1_living_conditions + b.Q5_vote_for_current_LC5council + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)

t.chair.good &lt;- felm.ri(lc5.chair.transformed ~ budget.treat | location.id | 0 | lc5.chair.name,
                        dta=budget.good.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                        rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>t.chair.bad &lt;- felm.ri(lc5.chair.transformed ~ budget.treat | location.id | 0 | lc5.chair.name, 
                       dta=budget.bad.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                       rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>t.chair.good.cov &lt;- felm.ri(lc5.chair.transformed ~ budget.treat + b.Q1_living_conditions + b.Q6_vote_for_current_LC5chair + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name,
                            dta=budget.good.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                            rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>t.chair.bad.cov &lt;- felm.ri(lc5.chair.transformed ~ budget.treat + b.Q1_living_conditions + b.Q6_vote_for_current_LC5chair + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, 
                           dta=budget.bad.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                           rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>t.coun.good &lt;- felm.ri(lc5.councillor.transformed ~ budget.treat | location.id | 0 | lc5.councillor.name,
                       dta=budget.good.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                       rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>t.coun.bad &lt;- felm.ri(lc5.councillor.transformed ~ budget.treat | location.id | 0 | lc5.councillor.name, 
                      dta=budget.bad.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                      rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>t.coun.good.cov &lt;- felm.ri(lc5.councillor.transformed ~ budget.treat + b.Q1_living_conditions + b.Q5_vote_for_current_LC5council + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name,
                           dta=budget.good.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                           rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>t.coun.bad.cov &lt;- felm.ri(lc5.councillor.transformed ~ budget.treat + b.Q1_living_conditions + b.Q5_vote_for_current_LC5council + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, 
                          dta=budget.bad.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                          rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>stargazer(h1a.b.1, h1a.b.1c, h1a.b.2, h1a.b.2c, h1b.b.1, h1b.b.1c, h1b.b.2, h1b.b.2c, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(4,4),
          keep = c(&quot;budget.treat&quot;),
          covariate.labels = c(&quot;Budget Treatment (RI)&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          notes = &quot;Notes: SEs derived from randomization inference for treatment and clustered by politician for all other covariates; one-tailed tests on treatment as pre-registered; contested elections where incumbent did not switch parties only.&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          se = list(c(t.chair.good$se),
                    c(t.chair.good.cov$se),
                    c(t.coun.good$se),
                    c(t.coun.good.cov$se),
                    c(t.chair.bad$se),
                    c(t.chair.bad.cov$se),
                    c(t.coun.bad$se),
                    c(t.coun.bad.cov$se)
          ),
          p = c(t.chair.good$p.one.way.greater,
                t.chair.good.cov$p.one.way.greater,
                t.coun.good$p.one.way.greater,
                t.coun.good.cov$p.one.way.greater,
                t.chair.bad$p.one.way.lesser,
                t.chair.bad.cov$p.one.way.lesser,
                t.coun.bad$p.one.way.lesser,
                t.coun.bad.cov$p.one.way.lesser),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Date and time: Fri, Dec 08, 2017 - 07:46:55</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 19:44:14
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{8}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-9} 
## \\[-1.8ex] &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} \\ 
##  &amp; \multicolumn{4}{c}{Good News} &amp; \multicolumn{4}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4) &amp; (5) &amp; (6) &amp; (7) &amp; (8)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment (RI) &amp; 0.005 &amp; 0.003 &amp; 0.006 &amp; 0.028 &amp; $-$0.019 &amp; 0.009 &amp; $-$0.042 &amp; $-$0.036 \\ 
##   &amp; (0.024) &amp; (0.017) &amp; (0.027) &amp; (0.018) &amp; (0.024) &amp; (0.016) &amp; (0.025) &amp; (0.016) \\ 
##   &amp; p = 0.421 &amp; p = 0.439 &amp; p = 0.411 &amp; p = 0.061 &amp; p = 0.216 &amp; p = 0.714 &amp; p = 0.051 &amp; p = 0.012 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes \\ 
## Observations &amp; 3,921 &amp; 3,921 &amp; 3,074 &amp; 3,074 &amp; 3,131 &amp; 3,131 &amp; 2,883 &amp; 2,883 \\ 
## Adjusted R$^{2}$ &amp; 0.092 &amp; 0.564 &amp; 0.181 &amp; 0.635 &amp; 0.265 &amp; 0.688 &amp; 0.255 &amp; 0.691 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{9}{r}{Notes: SEs derived from randomization inference for treatment and clustered by politician for all other covariates; one-tailed tests on treatment as pre-registered; contested elections where incumbent did not switch parties only.} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S17: Effect of budget treatment and treatment density on vote choice for district (LCV) incumbents, estimated as pre-specified
######################################################################################

b.ri &lt;- data.matrix(Budget_RI)
d.ri &lt;- data.matrix(Density_RI)
#Note: NAs introduced by coercion warning generated because &quot;district&quot; and &quot;village&quot; variables cannot be made numeric
#Note: these NAs are excluded when only the treatment variables are pasted into the final &quot;BD_RI&quot; dataframe in the block below

BD_RI &lt;- matrix(paste(b.ri,d.ri,sep=&quot;.&quot;),nrow=16083,ncol=10003)
BD_RI[BD_RI==&quot;0.NA&quot; | BD_RI==&quot;1.NA&quot;] &lt;- NA
BD_RI[BD_RI==&quot;0. 0&quot;] &lt;- &quot;0.0&quot;
BD_RI[BD_RI==&quot;0. 1&quot;] &lt;- &quot;0.1&quot;
BD_RI[BD_RI==&quot;1. 0&quot;] &lt;- &quot;1.0&quot;
BD_RI[BD_RI==&quot;1. 1&quot;] &lt;- &quot;1.1&quot;
BD_RI &lt;- as.data.frame(BD_RI)
BD_RI &lt;- cbind(Budget_RI[,1:3],BD_RI[,4:ncol(BD_RI)])
remove(b.ri,d.ri)

h11a.b.1 &lt;- felm(lc5.chair.transformed ~ bd.multi.treat + b.Q6_vote_for_current_LC5chair  + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | d.block | 0 | lc5.chair.name, data=budget.good.lc5.chair.comp)
h11a.b.2 &lt;- felm(lc5.councillor.transformed ~ bd.multi.treat + b.Q5_vote_for_current_LC5council  + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | d.block | 0 | lc5.councillor.name, data=budget.good.lc5.councillor.comp)
h11b.b.1 &lt;- felm(lc5.chair.transformed ~ bd.multi.treat + b.Q6_vote_for_current_LC5chair  + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | d.block | 0 | lc5.chair.name, data=budget.bad.lc5.chair.comp)
h11b.b.2 &lt;- felm(lc5.councillor.transformed ~ bd.multi.treat + b.Q5_vote_for_current_LC5council  + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | d.block | 0 | lc5.councillor.name, data=budget.bad.lc5.councillor.comp)

chair.good.crossed.pap &lt;- felm.ri2(lc5.chair.transformed ~ bd.multi.treat2 + b.Q6_vote_for_current_LC5chair  + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | d.block | 0 | lc5.chair.name,
                                   dta=budget.good.lc5.chair.comp, treat.var = &quot;bd.multi.treat2&quot;, rand.ob=BD_RI,
                                   rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.good.crossed.pap &lt;- felm.ri2(lc5.councillor.transformed ~ bd.multi.treat2 + b.Q5_vote_for_current_LC5council  + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | d.block | 0 | lc5.councillor.name, 
                                  dta=budget.good.lc5.councillor.comp, treat.var = &quot;bd.multi.treat2&quot;, rand.ob=BD_RI,
                                  rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.bad.crossed.pap &lt;- felm.ri2(lc5.chair.transformed ~ bd.multi.treat2 + b.Q6_vote_for_current_LC5chair  + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | d.block | 0 | lc5.chair.name,
                                  dta=budget.bad.lc5.chair.comp, treat.var = &quot;bd.multi.treat2&quot;, rand.ob=BD_RI,
                                  rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.bad.crossed.pap &lt;- felm.ri2(lc5.councillor.transformed ~ bd.multi.treat2 + b.Q5_vote_for_current_LC5council  + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | d.block | 0 | lc5.councillor.name, 
                                 dta=budget.bad.lc5.councillor.comp, treat.var = &quot;bd.multi.treat2&quot;, rand.ob=BD_RI,
                                 rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>stargazer(h11a.b.1, h11a.b.2, h11b.b.1, h11b.b.2, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(2,2),
          keep = c(&quot;bd.multi.treatbudget0.density1&quot;,&quot;bd.multi.treatbudget1.density0&quot;,&quot;bd.multi.treatbudget1.density1&quot;),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          covariate.labels = c(&quot;Control, High Density (RI)&quot;,&quot;Treated, Low Density (RI)&quot;, &quot;Treated, High Density (RI)&quot;),
          notes = &quot;Notes: SEs derived from RI; one-tailed tests as pre-registered; contested elections only&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          se = list(chair.good.crossed.pap$se,coun.good.crossed.pap$se,chair.bad.crossed.pap$se,coun.bad.crossed.pap$se),
          p = list(chair.good.crossed.pap$p.one.way.greater, coun.good.crossed.pap$p.one.way.greater, chair.bad.crossed.pap$p.one.way.lesser, coun.bad.crossed.pap$p.one.way.lesser),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Date and time: Sat, Dec 09, 2017 - 12:32:30</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 20:53:28
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{4}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-5} 
## \\[-1.8ex] &amp; LC5 Chair &amp; LC5 Councillor &amp; LC5 Chair &amp; LC5 Councillor \\ 
##  &amp; \multicolumn{2}{c}{Good News} &amp; \multicolumn{2}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4)\\ 
## \hline \\[-1.8ex] 
##  Control, High Density (RI) &amp; 0.004 &amp; $-$0.028 &amp; $-$0.035 &amp; $-$0.044 \\ 
##   &amp; (0.029) &amp; (0.050) &amp; (0.029) &amp; (0.055) \\ 
##   &amp; p = 0.455 &amp; p = 0.718 &amp; p = 0.114 &amp; p = 0.207 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Treated, Low Density (RI) &amp; 0.023 &amp; 0.012 &amp; $-$0.028 &amp; $-$0.054 \\ 
##   &amp; (0.026) &amp; (0.030) &amp; (0.025) &amp; (0.028) \\ 
##   &amp; p = 0.187 &amp; p = 0.349 &amp; p = 0.138 &amp; p = 0.028 \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Treated, High Density (RI) &amp; $-$0.010 &amp; $-$0.020 &amp; $-$0.030 &amp; $-$0.091 \\ 
##   &amp; (0.021) &amp; (0.045) &amp; (0.021) &amp; (0.050) \\ 
##   &amp; p = 0.684 &amp; p = 0.669 &amp; p = 0.078 &amp; p = 0.035 \\ 
##   &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Observations &amp; 3,281 &amp; 2,585 &amp; 2,633 &amp; 2,391 \\ 
## Adjusted R$^{2}$ &amp; 0.570 &amp; 0.596 &amp; 0.668 &amp; 0.624 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{5}{r}{Notes: SEs derived from RI; one-tailed tests as pre-registered; contested elections only} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S18: Conditional effects of budget treatment on votes for incumbents based on party switching
######################################################################################

h1a.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat*lc5.chair.party.switch + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=filter(budget.good, lc5.chair.competitive==1))
p1 &lt;- ifelse(h1a.b.1$coef[1]&gt;0, summary(h1a.b.1)$coef[1,4]/2, 1-summary(h1a.b.1)$coef[1,4]/2)

h1a.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*lc5.councillor.party.switch + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=filter(budget.good, lc5.councillor.competitive==1))
p2 &lt;- ifelse(h1a.b.2$coef[1]&gt;0, summary(h1a.b.2)$coef[1,4]/2, 1-summary(h1a.b.2)$coef[1,4]/2)

h1b.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat*lc5.chair.party.switch + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=filter(budget.bad, lc5.chair.competitive==1))
p3 &lt;- ifelse(h1b.b.1$coef[1]&lt;0, summary(h1b.b.1)$coef[1,4]/2, 1-summary(h1b.b.1)$coef[1,4]/2)

h1b.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*lc5.councillor.party.switch + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=filter(budget.bad, lc5.councillor.competitive==1))
p4 &lt;- ifelse(h1b.b.2$coef[1]&lt;0, summary(h1b.b.2)$coef[1,4]/2, 1-summary(h1b.b.2)$coef[1,4]/2)

h1a.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat*lc5.chair.party.switch + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=filter(budget.good, lc5.chair.competitive==1))
p5 &lt;- ifelse(h1a.b.1c$coef[1]&gt;0, summary(h1a.b.1c)$coef[1,4]/2, 1-summary(h1a.b.1c)$coef[1,4]/2)

h1a.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*lc5.councillor.party.switch + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=filter(budget.good, lc5.councillor.competitive==1))
p6 &lt;- ifelse(h1a.b.2c$coef[1]&gt;0, summary(h1a.b.2c)$coef[1,4]/2, 1-summary(h1a.b.2c)$coef[1,4]/2)

h1b.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat*lc5.chair.party.switch + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=filter(budget.bad, lc5.chair.competitive==1))
p7 &lt;- ifelse(h1b.b.1c$coef[1]&lt;0, summary(h1b.b.1c)$coef[1,4]/2, 1-summary(h1b.b.1c)$coef[1,4]/2)

h1b.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat*lc5.councillor.party.switch + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=filter(budget.bad, lc5.councillor.competitive==1))
p8 &lt;- ifelse(h1b.b.2c$coef[1]&lt;0, summary(h1b.b.2c)$coef[1,4]/2, 1-summary(h1b.b.2c)$coef[1,4]/2)

stargazer(h1a.b.1, h1a.b.1c, h1a.b.2, h1a.b.2c, h1b.b.1, h1b.b.1c, h1b.b.2, h1b.b.2c, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(4,4),
          keep = c(&quot;budget.treat&quot;,&quot;lc5.chair.intent&quot;,&quot;lc5.councillor.intent&quot;,&quot;budget.treat:lc5.chair.party.switch&quot;,&quot;budget.treat:lc5.councillor.party.switch&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          covariate.labels = c(&quot;Budget Treatment&quot;,&quot;LC5 Chair Intent&quot;,&quot;Budget Treatment X Chair Party Switch&quot;,&quot;LC5 Councillor Intent&quot;, &quot;Budget Treatment X Councillor Party Switch&quot;),
          notes = &quot;Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          p = list(p1,p5,p2,p6,p3,p7,p4,p8),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Tue, Mar 13, 2018 - 16:00:04</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 20:53:29
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{8}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-9} 
## \\[-1.8ex] &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} \\ 
##  &amp; \multicolumn{4}{c}{Good News} &amp; \multicolumn{4}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4) &amp; (5) &amp; (6) &amp; (7) &amp; (8)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment &amp; 0.002 &amp; 0.003 &amp; 0.025 &amp; 0.027 &amp; 0.011 &amp; 0.009 &amp; $-$0.031 &amp; $-$0.032 \\ 
##   &amp; (0.018) &amp; (0.017) &amp; (0.016) &amp; (0.016) &amp; (0.016) &amp; (0.017) &amp; (0.014) &amp; (0.014) \\ 
##   &amp; p = 0.456 &amp; p = 0.439 &amp; p = 0.059 &amp; p = 0.054 &amp; p = 0.744 &amp; p = 0.714 &amp; p = 0.013 &amp; p = 0.009 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Chair Intent &amp; 0.072 &amp; 0.066 &amp;  &amp;  &amp; 0.035 &amp; 0.032 &amp;  &amp;  \\ 
##   &amp; (0.035) &amp; (0.030) &amp;  &amp;  &amp; (0.022) &amp; (0.022) &amp;  &amp;  \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Budget Treatment X Chair Party Switch &amp; 0.027 &amp; 0.032 &amp;  &amp;  &amp; 0.016 &amp; 0.030 &amp;  &amp;  \\ 
##   &amp; (0.016) &amp; (0.017) &amp;  &amp;  &amp; (0.016) &amp; (0.018) &amp;  &amp;  \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Councillor Intent &amp;  &amp;  &amp; 0.055 &amp; 0.047 &amp;  &amp;  &amp; 0.006 &amp; 0.008 \\ 
##   &amp;  &amp;  &amp; (0.015) &amp; (0.015) &amp;  &amp;  &amp; (0.016) &amp; (0.015) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  Budget Treatment X Councillor Party Switch &amp;  &amp;  &amp; $-$0.098 &amp; $-$0.091 &amp;  &amp;  &amp; 0.058 &amp; 0.057 \\ 
##   &amp;  &amp;  &amp; (0.040) &amp; (0.043) &amp;  &amp;  &amp; (0.036) &amp; (0.036) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes \\ 
## Observations &amp; 4,398 &amp; 4,398 &amp; 3,545 &amp; 3,545 &amp; 3,333 &amp; 3,333 &amp; 3,687 &amp; 3,687 \\ 
## Adjusted R$^{2}$ &amp; 0.243 &amp; 0.248 &amp; 0.350 &amp; 0.355 &amp; 0.342 &amp; 0.346 &amp; 0.482 &amp; 0.484 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{9}{r}{Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S19: Effects of budget treatment on votes for individual incumbents who ran for re-election as members of the same party
######################################################################################

h1a.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=filter(budget.good.lc5.chair.comp, lc5.chair.ran2016==TRUE))
p1 &lt;- ifelse(h1a.b.1$coef[1]&gt;0, summary(h1a.b.1)$coef[1,4]/2, 1-summary(h1a.b.1)$coef[1,4]/2)

h1a.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=filter(budget.good.lc5.councillor.comp, lc5.councillor.ran2016==TRUE))
p2 &lt;- ifelse(h1a.b.2$coef[1]&gt;0, summary(h1a.b.2)$coef[1,4]/2, 1-summary(h1a.b.2)$coef[1,4]/2)

h1b.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=filter(budget.bad.lc5.chair.comp, lc5.chair.ran2016==TRUE))
p3 &lt;- ifelse(h1b.b.1$coef[1]&lt;0, summary(h1b.b.1)$coef[1,4]/2, 1-summary(h1b.b.1)$coef[1,4]/2)

h1b.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=filter(budget.bad.lc5.councillor.comp, lc5.councillor.ran2016==TRUE))
p4 &lt;- ifelse(h1b.b.2$coef[1]&lt;0, summary(h1b.b.2)$coef[1,4]/2, 1-summary(h1b.b.2)$coef[1,4]/2)

h1a.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=filter(budget.good.lc5.chair.comp, lc5.chair.ran2016==TRUE))
p5 &lt;- ifelse(h1a.b.1c$coef[1]&gt;0, summary(h1a.b.1c)$coef[1,4]/2, 1-summary(h1a.b.1c)$coef[1,4]/2)

h1a.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=filter(budget.good.lc5.councillor.comp, lc5.councillor.ran2016==TRUE))
p6 &lt;- ifelse(h1a.b.2c$coef[1]&gt;0, summary(h1a.b.2c)$coef[1,4]/2, 1-summary(h1a.b.2c)$coef[1,4]/2)

h1b.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=filter(budget.bad.lc5.chair.comp, lc5.chair.ran2016==TRUE))
p7 &lt;- ifelse(h1b.b.1c$coef[1]&lt;0, summary(h1b.b.1c)$coef[1,4]/2, 1-summary(h1b.b.1c)$coef[1,4]/2)

h1b.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=filter(budget.bad.lc5.councillor.comp, lc5.councillor.ran2016==TRUE))
p8 &lt;- ifelse(h1b.b.2c$coef[1]&lt;0, summary(h1b.b.2c)$coef[1,4]/2, 1-summary(h1b.b.2c)$coef[1,4]/2)

stargazer(h1a.b.1, h1a.b.1c, h1a.b.2, h1a.b.2c, h1b.b.1, h1b.b.1c, h1b.b.2, h1b.b.2c, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(4,4),
          keep = c(&quot;budget.treat&quot;,&quot;lc5.chair.intent&quot;,&quot;lc5.councillor.intent&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          covariate.labels = c(&quot;Budget Treatment&quot;,&quot;LC5 Chair Intent&quot;,&quot;LC5 Councillor Intent&quot;),
          notes = &quot;Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          p = list(p1,p5,p2,p6,p3,p7,p4,p8),
          se = list(h1a.b.1$cse[1:2],
                    h1a.b.1c$cse[1:2],
                    h1a.b.2$cse[1:2],
                    h1a.b.2c$cse[1:2],
                    h1b.b.1$cse[1:2],
                    h1b.b.1c$cse[1:2],
                    h1b.b.2$cse[1:2],
                    h1b.b.2c$cse[1:2]),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Date and time: Sat, Dec 09, 2017 - 09:38:56</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 20:53:30
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{8}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-9} 
## \\[-1.8ex] &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} \\ 
##  &amp; \multicolumn{4}{c}{Good News} &amp; \multicolumn{4}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4) &amp; (5) &amp; (6) &amp; (7) &amp; (8)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment &amp; 0.0004 &amp; 0.001 &amp; 0.035 &amp; 0.041 &amp; 0.040 &amp; 0.040 &amp; $-$0.026 &amp; $-$0.022 \\ 
##   &amp; (0.019) &amp; (0.018) &amp; (0.026) &amp; (0.028) &amp; (0.027) &amp; (0.027) &amp; (0.019) &amp; (0.019) \\ 
##   &amp; p = 0.491 &amp; p = 0.469 &amp; p = 0.090 &amp; p = 0.071 &amp; p = 0.933 &amp; p = 0.934 &amp; p = 0.080 &amp; p = 0.130 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Chair Intent &amp; 0.089 &amp; 0.083 &amp;  &amp;  &amp; 0.074 &amp; 0.077 &amp;  &amp;  \\ 
##   &amp; (0.042) &amp; (0.037) &amp;  &amp;  &amp; (0.019) &amp; (0.019) &amp;  &amp;  \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Councillor Intent &amp;  &amp;  &amp; 0.083 &amp; 0.081 &amp;  &amp;  &amp; $-$0.013 &amp; $-$0.009 \\ 
##   &amp;  &amp;  &amp; (0.025) &amp; (0.025) &amp;  &amp;  &amp; (0.024) &amp; (0.022) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes \\ 
## Observations &amp; 3,520 &amp; 3,520 &amp; 1,331 &amp; 1,331 &amp; 1,248 &amp; 1,248 &amp; 1,461 &amp; 1,461 \\ 
## Adjusted R$^{2}$ &amp; 0.204 &amp; 0.210 &amp; 0.356 &amp; 0.368 &amp; 0.361 &amp; 0.363 &amp; 0.340 &amp; 0.342 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{9}{r}{Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S20: Effects of budget treatment on votes for district incumbents, with incumbency defined as the party previously elected to the seat
######################################################################################

h1a.b.1 &lt;- felm(lc5.chair.inc.vote.orig ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=filter(budget.good, lc5.chair.competitive==1))
p1 &lt;- ifelse(h1a.b.1$coef[1]&gt;0, summary(h1a.b.1)$coef[1,4]/2, 1-summary(h1a.b.1)$coef[1,4]/2)

h1a.b.2 &lt;- felm(lc5.councillor.inc.vote.orig ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=filter(budget.good, lc5.councillor.competitive==1  &amp; is.na(lc5.councillor.redistricted2016)))
p2 &lt;- ifelse(h1a.b.2$coef[1]&gt;0, summary(h1a.b.2)$coef[1,4]/2, 1-summary(h1a.b.2)$coef[1,4]/2)

h1b.b.1 &lt;- felm(lc5.chair.inc.vote.orig ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=filter(budget.bad, lc5.chair.competitive==1))
p3 &lt;- ifelse(h1b.b.1$coef[1]&lt;0, summary(h1b.b.1)$coef[1,4]/2, 1-summary(h1b.b.1)$coef[1,4]/2)

h1b.b.2 &lt;- felm(lc5.councillor.inc.vote.orig ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=filter(budget.bad, lc5.councillor.competitive==1  &amp; is.na(lc5.councillor.redistricted2016)))
p4 &lt;- ifelse(h1b.b.2$coef[1]&lt;0, summary(h1b.b.2)$coef[1,4]/2, 1-summary(h1b.b.2)$coef[1,4]/2)

h1a.b.1c &lt;- felm(lc5.chair.inc.vote.orig ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=filter(budget.good, lc5.chair.competitive==1))
p5 &lt;- ifelse(h1a.b.1c$coef[1]&gt;0, summary(h1a.b.1c)$coef[1,4]/2, 1-summary(h1a.b.1c)$coef[1,4]/2)

h1a.b.2c &lt;- felm(lc5.councillor.inc.vote.orig ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=filter(budget.good, lc5.councillor.competitive==1 &amp; is.na(lc5.councillor.redistricted2016)))
p6 &lt;- ifelse(h1a.b.2c$coef[1]&gt;0, summary(h1a.b.2c)$coef[1,4]/2, 1-summary(h1a.b.2c)$coef[1,4]/2)

h1b.b.1c &lt;- felm(lc5.chair.inc.vote.orig ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=filter(budget.bad, lc5.chair.competitive==1))
p7 &lt;- ifelse(h1b.b.1c$coef[1]&lt;0, summary(h1b.b.1c)$coef[1,4]/2, 1-summary(h1b.b.1c)$coef[1,4]/2)

h1b.b.2c &lt;- felm(lc5.councillor.inc.vote.orig ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=filter(budget.bad, lc5.councillor.competitive==1 &amp; is.na(lc5.councillor.redistricted2016)))
p8 &lt;- ifelse(h1b.b.2c$coef[1]&lt;0, summary(h1b.b.2c)$coef[1,4]/2, 1-summary(h1b.b.2c)$coef[1,4]/2)

stargazer(h1a.b.1, h1a.b.1c, h1a.b.2, h1a.b.2c, h1b.b.1, h1b.b.1c, h1b.b.2, h1b.b.2c, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(4,4),
          keep = c(&quot;budget.treat&quot;,&quot;lc5.chair.intent&quot;,&quot;lc5.councillor.intent&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          covariate.labels = c(&quot;Budget Treatment&quot;,&quot;LC5 Chair Intent&quot;,&quot;LC5 Councillor Intent&quot;),
          notes = &quot;Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          p = list(p1,p5,p2,p6,p3,p7,p4,p8),
          se = list(h1a.b.1$cse[1:2],
                    h1a.b.1c$cse[1:2],
                    h1a.b.2$cse[1:2],
                    h1a.b.2c$cse[1:2],
                    h1b.b.1$cse[1:2],
                    h1b.b.1c$cse[1:2],
                    h1b.b.2$cse[1:2],
                    h1b.b.2c$cse[1:2]),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) #Date and time: Sat, Dec 09, 2017 - 09:51:49</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 20:53:32
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{8}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-9} 
## \\[-1.8ex] &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} \\ 
##  &amp; \multicolumn{4}{c}{Good News} &amp; \multicolumn{4}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4) &amp; (5) &amp; (6) &amp; (7) &amp; (8)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment &amp; 0.001 &amp; 0.0004 &amp; 0.029 &amp; 0.030 &amp; 0.008 &amp; 0.007 &amp; $-$0.033 &amp; $-$0.034 \\ 
##   &amp; (0.016) &amp; (0.016) &amp; (0.016) &amp; (0.016) &amp; (0.016) &amp; (0.016) &amp; (0.013) &amp; (0.013) \\ 
##   &amp; p = 0.467 &amp; p = 0.490 &amp; p = 0.030 &amp; p = 0.031 &amp; p = 0.691 &amp; p = 0.683 &amp; p = 0.006 &amp; p = 0.004 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Chair Intent &amp; 0.072 &amp; 0.064 &amp;  &amp;  &amp; 0.034 &amp; 0.031 &amp;  &amp;  \\ 
##   &amp; (0.034) &amp; (0.030) &amp;  &amp;  &amp; (0.023) &amp; (0.022) &amp;  &amp;  \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Councillor Intent &amp;  &amp;  &amp; 0.054 &amp; 0.045 &amp;  &amp;  &amp; 0.0005 &amp; 0.002 \\ 
##   &amp;  &amp;  &amp; (0.014) &amp; (0.015) &amp;  &amp;  &amp; (0.016) &amp; (0.016) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes \\ 
## Observations &amp; 4,398 &amp; 4,398 &amp; 3,517 &amp; 3,517 &amp; 3,333 &amp; 3,333 &amp; 3,621 &amp; 3,621 \\ 
## Adjusted R$^{2}$ &amp; 0.180 &amp; 0.186 &amp; 0.320 &amp; 0.325 &amp; 0.416 &amp; 0.418 &amp; 0.389 &amp; 0.392 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{9}{r}{Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S21: Effects of budget treatment on votes for district incumbents, with incumbency defined by individual if the individual is running for re-election
######################################################################################

#Note: this is the same as Table S18 without the interaction term for party switching

h1a.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=filter(budget.good, lc5.chair.competitive==1))
p1 &lt;- ifelse(h1a.b.1$coef[1]&gt;0, summary(h1a.b.1)$coef[1,4]/2, 1-summary(h1a.b.1)$coef[1,4]/2)

h1a.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=filter(budget.good, lc5.councillor.competitive==1))
p2 &lt;- ifelse(h1a.b.2$coef[1]&gt;0, summary(h1a.b.2)$coef[1,4]/2, 1-summary(h1a.b.2)$coef[1,4]/2)

h1b.b.1 &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, data=filter(budget.bad, lc5.chair.competitive==1))
p3 &lt;- ifelse(h1b.b.1$coef[1]&lt;0, summary(h1b.b.1)$coef[1,4]/2, 1-summary(h1b.b.1)$coef[1,4]/2)

h1b.b.2 &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, data=filter(budget.bad, lc5.councillor.competitive==1))
p4 &lt;- ifelse(h1b.b.2$coef[1]&lt;0, summary(h1b.b.2)$coef[1,4]/2, 1-summary(h1b.b.2)$coef[1,4]/2)

h1a.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=filter(budget.good, lc5.chair.competitive==1))
p5 &lt;- ifelse(h1a.b.1c$coef[1]&gt;0, summary(h1a.b.1c)$coef[1,4]/2, 1-summary(h1a.b.1c)$coef[1,4]/2)

h1a.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=filter(budget.good, lc5.councillor.competitive==1))
p6 &lt;- ifelse(h1a.b.2c$coef[1]&gt;0, summary(h1a.b.2c)$coef[1,4]/2, 1-summary(h1a.b.2c)$coef[1,4]/2)

h1b.b.1c &lt;- felm(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, data=filter(budget.bad, lc5.chair.competitive==1))
p7 &lt;- ifelse(h1b.b.1c$coef[1]&lt;0, summary(h1b.b.1c)$coef[1,4]/2, 1-summary(h1b.b.1c)$coef[1,4]/2)

h1b.b.2c &lt;- felm(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, data=filter(budget.bad, lc5.councillor.competitive==1))
p8 &lt;- ifelse(h1b.b.2c$coef[1]&lt;0, summary(h1b.b.2c)$coef[1,4]/2, 1-summary(h1b.b.2c)$coef[1,4]/2)

stargazer(h1a.b.1, h1a.b.1c, h1a.b.2, h1a.b.2c, h1b.b.1, h1b.b.1c, h1b.b.2, h1b.b.2c, type = &quot;latex&quot;, 
          dep.var.labels = c(&quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;, &quot;LC5 Chair&quot;, &quot;LC5 Councillor&quot;),
          column.labels = c(&quot;Good News&quot;,&quot;Bad News&quot;), column.separate = c(4,4),
          keep = c(&quot;budget.treat&quot;,&quot;lc5.chair.intent&quot;,&quot;lc5.councillor.intent&quot;),
          add.lines = list(c(&quot;Village fixed effects&quot;, &quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;, &quot;Yes&quot;,&quot;Yes&quot;,&quot;Yes&quot;),
                           c(&quot;Covariates&quot;, &quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;,&quot;No&quot;,&quot;Yes&quot;)),
          dep.var.caption  = &quot;DV: Vote Choice for the Incumbent&quot;,
          covariate.labels = c(&quot;Budget Treatment&quot;,&quot;LC5 Chair Intent&quot;,&quot;LC5 Councillor Intent&quot;),
          notes = &quot;Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only&quot;,
          df = FALSE, omit.stat = c(&quot;rsq&quot;,&quot;ser&quot;),
          p = list(p1,p5,p2,p6,p3,p7,p4,p8),
          se = list(h1a.b.1$cse[1:2],
                    h1a.b.1c$cse[1:2],
                    h1a.b.2$cse[1:2],
                    h1a.b.2c$cse[1:2],
                    h1b.b.1$cse[1:2],
                    h1b.b.1c$cse[1:2],
                    h1b.b.2$cse[1:2],
                    h1b.b.2c$cse[1:2]),
          report = c('vcsp'),
          notes.append = FALSE,
          notes.label = &quot;&quot;
) # Date and time: Sat, Dec 09, 2017 - 10:16:04</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 20:53:33
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{8}{c}{DV: Vote Choice for the Incumbent} \\ 
## \cline{2-9} 
## \\[-1.8ex] &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} &amp; \multicolumn{2}{c}{LC5 Chair} &amp; \multicolumn{2}{c}{LC5 Councillor} \\ 
##  &amp; \multicolumn{4}{c}{Good News} &amp; \multicolumn{4}{c}{Bad News} \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4) &amp; (5) &amp; (6) &amp; (7) &amp; (8)\\ 
## \hline \\[-1.8ex] 
##  Budget Treatment &amp; 0.005 &amp; 0.006 &amp; 0.013 &amp; 0.016 &amp; 0.012 &amp; 0.011 &amp; $-$0.018 &amp; $-$0.020 \\ 
##   &amp; (0.016) &amp; (0.016) &amp; (0.015) &amp; (0.015) &amp; (0.016) &amp; (0.016) &amp; (0.013) &amp; (0.013) \\ 
##   &amp; p = 0.379 &amp; p = 0.353 &amp; p = 0.188 &amp; p = 0.154 &amp; p = 0.777 &amp; p = 0.764 &amp; p = 0.076 &amp; p = 0.059 \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Chair Intent &amp; 0.072 &amp; 0.066 &amp;  &amp;  &amp; 0.035 &amp; 0.032 &amp;  &amp;  \\ 
##   &amp; (0.035) &amp; (0.031) &amp;  &amp;  &amp; (0.022) &amp; (0.022) &amp;  &amp;  \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
##  LC5 Councillor Intent &amp;  &amp;  &amp; 0.056 &amp; 0.047 &amp;  &amp;  &amp; 0.006 &amp; 0.008 \\ 
##   &amp;  &amp;  &amp; (0.015) &amp; (0.015) &amp;  &amp;  &amp; (0.016) &amp; (0.015) \\ 
##   &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Village fixed effects &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes &amp; Yes \\ 
## Covariates &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes &amp; No &amp; Yes \\ 
## Observations &amp; 4,398 &amp; 4,398 &amp; 3,545 &amp; 3,545 &amp; 3,333 &amp; 3,333 &amp; 3,687 &amp; 3,687 \\ 
## Adjusted R$^{2}$ &amp; 0.243 &amp; 0.248 &amp; 0.350 &amp; 0.355 &amp; 0.343 &amp; 0.346 &amp; 0.482 &amp; 0.483 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \multicolumn{9}{r}{Notes: SEs clustered by politician; one-tailed tests as pre-registered; contested elections only} \\ 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S23: Manipulation checks for updating in beliefs about budget performance of LC V
######################################################################################

data$manipulation.updating.loose &lt;- ifelse(data$budget.post.diff==0 | abs(data$budget.post.diff) &lt;= abs(data$budget.prior.diff), 1, 0)
data$directional.updating &lt;- ifelse(data$budget.post.diff==0 &amp; data$budget.prior.diff==0, 1, 0)
data$directional.updating &lt;- ifelse(data$budget.prior.diff&gt;0 &amp; data$budget.post &gt; data$budget.prior, 1, data$directional.updating)
data$directional.updating &lt;- ifelse(data$budget.prior.diff&lt;0 &amp; data$budget.post &lt; data$budget.prior, 1, data$directional.updating)
data$directional.updating &lt;- ifelse(is.na(data$budget.prior) | is.na(data$budget.post), NA, data$directional.updating)

##Strict updating
strict &lt;- felm(manipulation.strict ~ budget.treat |0|0|0, data=data)
summary(strict)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.strict ~ budget.treat | 0 | 0 | 0,      data = data) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.2036 -0.2036 -0.1970 -0.1970  0.8030 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)  0.197002   0.005109  38.557   &lt;2e-16 ***
## budget.treat 0.006580   0.007192   0.915     0.36    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4003 on 12388 degrees of freedom
##   (3693 observations deleted due to missingness)
## Multiple R-squared(full model): 6.757e-05   Adjusted R-squared: -1.314e-05 
## Multiple R-squared(proj model): 6.757e-05   Adjusted R-squared: -1.314e-05 
## F-statistic(full model):0.8372 on 1 and 12388 DF, p-value: 0.3602 
## F-statistic(proj model): 0.8372 on 1 and 12388 DF, p-value: 0.3602</code></pre>
<pre class="r"><code>strict.fe &lt;- felm(manipulation.strict ~ budget.treat |location.id|0|0, data=data)
summary(strict.fe)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.strict ~ budget.treat | location.id |      0 | 0, data = data) 
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.85868 -0.22302 -0.05506  0.00085  0.97730 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)
## budget.treat 0.003579   0.008023   0.446    0.656
## 
## Residual standard error: 0.3679 on 11646 degrees of freedom
##   (3693 observations deleted due to missingness)
## Multiple R-squared(full model): 0.2057   Adjusted R-squared: 0.155 
## Multiple R-squared(proj model): 1.709e-05   Adjusted R-squared: -0.06378 
## F-statistic(full model):4.058 on 743 and 11646 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model): 0.199 on 1 and 11646 DF, p-value: 0.6555</code></pre>
<pre class="r"><code>strict.fe.c &lt;- felm(manipulation.strict ~ budget.treat |location.id|0|district_lower, data=data)
summary(strict.fe.c)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.strict ~ budget.treat | location.id |      0 | district_lower, data = data) 
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.85868 -0.22302 -0.05506  0.00085  0.97730 
## 
## Coefficients:
##              Estimate Cluster s.e. t value Pr(&gt;|t|)
## budget.treat 0.003579     0.007885   0.454     0.65
## 
## Residual standard error: 0.3679 on 11646 degrees of freedom
##   (3693 observations deleted due to missingness)
## Multiple R-squared(full model): 0.2057   Adjusted R-squared: 0.155 
## Multiple R-squared(proj model): 1.709e-05   Adjusted R-squared: -0.06378 
## F-statistic(full model, *iid*):4.058 on 743 and 11646 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model): 0.2061 on 1 and 26 DF, p-value: 0.6536</code></pre>
<pre class="r"><code>##Updating
updating &lt;- felm(manipulation.updating ~ budget.treat |0|0|0, data=data)
summary(updating)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.updating ~ budget.treat | 0 | 0 |      0, data = data) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.3877 -0.3877 -0.3767  0.6123  0.6233 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)  0.376664   0.006230  60.464   &lt;2e-16 ***
## budget.treat 0.011058   0.008772   1.261    0.207    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4859 on 12273 degrees of freedom
##   (3808 observations deleted due to missingness)
## Multiple R-squared(full model): 0.0001295   Adjusted R-squared: 4.799e-05 
## Multiple R-squared(proj model): 0.0001295   Adjusted R-squared: 4.799e-05 
## F-statistic(full model):1.589 on 1 and 12273 DF, p-value: 0.2075 
## F-statistic(proj model): 1.589 on 1 and 12273 DF, p-value: 0.2075</code></pre>
<pre class="r"><code>updating.fe &lt;- felm(manipulation.updating ~ budget.treat |location.id|0|0, data=data)
summary(updating.fe)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.updating ~ budget.treat | location.id |      0 | 0, data = data) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.8962 -0.3758 -0.2160  0.5014  0.9650 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)
## budget.treat 0.009408   0.010399   0.905    0.366
## 
## Residual standard error: 0.4744 on 11531 degrees of freedom
##   (3808 observations deleted due to missingness)
## Multiple R-squared(full model): 0.1046   Adjusted R-squared: 0.04693 
## Multiple R-squared(proj model): 7.099e-05   Adjusted R-squared: -0.06436 
## F-statistic(full model):1.813 on 743 and 11531 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model): 0.8186 on 1 and 11531 DF, p-value: 0.3656</code></pre>
<pre class="r"><code>updating.fe.c &lt;- felm(manipulation.updating ~ budget.treat |location.id|0|district_lower, data=data)
summary(updating.fe.c)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.updating ~ budget.treat | location.id |      0 | district_lower, data = data) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.8962 -0.3758 -0.2160  0.5014  0.9650 
## 
## Coefficients:
##              Estimate Cluster s.e. t value Pr(&gt;|t|)
## budget.treat 0.009408     0.009500    0.99    0.322
## 
## Residual standard error: 0.4744 on 11531 degrees of freedom
##   (3808 observations deleted due to missingness)
## Multiple R-squared(full model): 0.1046   Adjusted R-squared: 0.04693 
## Multiple R-squared(proj model): 7.099e-05   Adjusted R-squared: -0.06436 
## F-statistic(full model, *iid*):1.813 on 743 and 11531 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model): 0.9808 on 1 and 26 DF, p-value: 0.3311</code></pre>
<pre class="r"><code>##Loose updating
loose &lt;- felm(manipulation.updating.loose ~ budget.treat |0|0|0, data=data)
summary(loose)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.updating.loose ~ budget.treat | 0 |      0 | 0, data = data) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6748 -0.6600  0.3252  0.3400  0.3400 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)  0.659984   0.006039 109.281   &lt;2e-16 ***
## budget.treat 0.014814   0.008505   1.742   0.0815 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4711 on 12273 degrees of freedom
##   (3808 observations deleted due to missingness)
## Multiple R-squared(full model): 0.0002472   Adjusted R-squared: 0.0001657 
## Multiple R-squared(proj model): 0.0002472   Adjusted R-squared: 0.0001657 
## F-statistic(full model):3.034 on 1 and 12273 DF, p-value: 0.08154 
## F-statistic(proj model): 3.034 on 1 and 12273 DF, p-value: 0.08154</code></pre>
<pre class="r"><code>loose.fe &lt;- felm(manipulation.updating.loose ~ budget.treat |location.id|0|0, data=data)
summary(loose.fe)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.updating.loose ~ budget.treat | location.id |      0 | 0, data = data) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9646 -0.4901  0.2040  0.3366  0.8366 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)  
## budget.treat  0.01986    0.01004   1.977    0.048 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4583 on 11531 degrees of freedom
##   (3808 observations deleted due to missingness)
## Multiple R-squared(full model): 0.1112   Adjusted R-squared: 0.05389 
## Multiple R-squared(proj model): 0.000339   Adjusted R-squared: -0.06407 
## F-statistic(full model):1.941 on 743 and 11531 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model):  3.91 on 1 and 11531 DF, p-value: 0.04803</code></pre>
<pre class="r"><code>loose.fe.c &lt;- felm(manipulation.updating.loose ~ budget.treat |location.id|0|district_lower, data=data)
summary(loose.fe.c)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.updating.loose ~ budget.treat | location.id |      0 | district_lower, data = data) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9646 -0.4901  0.2040  0.3366  0.8366 
## 
## Coefficients:
##              Estimate Cluster s.e. t value Pr(&gt;|t|)  
## budget.treat 0.019862     0.007876   2.522   0.0117 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4583 on 11531 degrees of freedom
##   (3808 observations deleted due to missingness)
## Multiple R-squared(full model): 0.1112   Adjusted R-squared: 0.05389 
## Multiple R-squared(proj model): 0.000339   Adjusted R-squared: -0.06407 
## F-statistic(full model, *iid*):1.941 on 743 and 11531 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model):  6.36 on 1 and 26 DF, p-value: 0.01814</code></pre>
<pre class="r"><code>#Accounting for eligibility for divergence
data$diverge.eligble &lt;- 1
data$diverge.eligble &lt;- ifelse(data$budget.prior==1 &amp; data$budget.actual&gt;=4, 0, data$diverge.eligble)
data$diverge.eligble &lt;- ifelse(data$budget.prior==5 &amp; data$budget.actual&lt;=2, 0, data$diverge.eligble)
table(data$diverge.eligble)</code></pre>
<pre><code>## 
##     0     1 
##  1473 14412</code></pre>
<pre class="r"><code>loose.e &lt;- felm(manipulation.updating.loose ~ budget.treat |0|0|0, data=filter(data, diverge.eligble==1))
summary(loose.e)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.updating.loose ~ budget.treat | 0 |      0 | 0, data = filter(data, diverge.eligble == 1)) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6396 -0.6238  0.3604  0.3762  0.3762 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)  0.623750   0.006504  95.900   &lt;2e-16 ***
## budget.treat 0.015821   0.009163   1.727   0.0843 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4823 on 11082 degrees of freedom
##   (3328 observations deleted due to missingness)
## Multiple R-squared(full model): 0.0002689   Adjusted R-squared: 0.0001787 
## Multiple R-squared(proj model): 0.0002689   Adjusted R-squared: 0.0001787 
## F-statistic(full model):2.981 on 1 and 11082 DF, p-value: 0.08427 
## F-statistic(proj model): 2.981 on 1 and 11082 DF, p-value: 0.08427</code></pre>
<pre class="r"><code>loose.e.fe &lt;- felm(manipulation.updating.loose ~ budget.treat |location.id|0|0, data=filter(data, diverge.eligble==1))
summary(loose.e.fe)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.updating.loose ~ budget.treat | location.id |      0 | 0, data = filter(data, diverge.eligble == 1)) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9625 -0.4894  0.1967  0.3567  0.9291 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)  
## budget.treat  0.02114    0.01083   1.952    0.051 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4671 on 10343 degrees of freedom
##   (3328 observations deleted due to missingness)
## Multiple R-squared(full model): 0.1249   Adjusted R-squared: 0.06226 
## Multiple R-squared(proj model): 0.0003683   Adjusted R-squared: -0.07115 
## F-statistic(full model):1.994 on 740 and 10343 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model):  3.81 on 1 and 10343 DF, p-value: 0.05096</code></pre>
<pre class="r"><code>loose.e.fe.c &lt;- felm(manipulation.updating.loose ~ budget.treat |location.id|0|district_lower, data=filter(data, diverge.eligble==1))
summary(loose.e.fe.c)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = manipulation.updating.loose ~ budget.treat | location.id |      0 | district_lower, data = filter(data, diverge.eligble ==      1)) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9625 -0.4894  0.1967  0.3567  0.9291 
## 
## Coefficients:
##              Estimate Cluster s.e. t value Pr(&gt;|t|)   
## budget.treat 0.021139     0.008058   2.624  0.00872 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4671 on 10343 degrees of freedom
##   (3328 observations deleted due to missingness)
## Multiple R-squared(full model): 0.1249   Adjusted R-squared: 0.06226 
## Multiple R-squared(proj model): 0.0003683   Adjusted R-squared: -0.07115 
## F-statistic(full model, *iid*):1.994 on 740 and 10343 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model): 6.883 on 1 and 26 DF, p-value: 0.01437</code></pre>
<pre class="r"><code>##Directional updating
directional &lt;- felm(directional.updating ~ budget.treat |0|0|0, data=data)
summary(directional)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = directional.updating ~ budget.treat | 0 | 0 |      0, data = data) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.4241 -0.4241 -0.4120  0.5759  0.5880 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)  0.411978   0.006327  65.116   &lt;2e-16 ***
## budget.treat 0.012152   0.008912   1.364    0.173    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4932 on 12251 degrees of freedom
##   (3830 observations deleted due to missingness)
## Multiple R-squared(full model): 0.0001517   Adjusted R-squared: 7.012e-05 
## Multiple R-squared(proj model): 0.0001517   Adjusted R-squared: 7.012e-05 
## F-statistic(full model):1.859 on 1 and 12251 DF, p-value: 0.1727 
## F-statistic(proj model): 1.859 on 1 and 12251 DF, p-value: 0.1727</code></pre>
<pre class="r"><code>directional.fe &lt;- felm(directional.updating ~ budget.treat |location.id|0|0, data=data)
summary(directional.fe)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = directional.updating ~ budget.treat | location.id |      0 | 0, data = data) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.8935 -0.4021 -0.2021  0.4769  0.9656 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)
## budget.treat  0.01027    0.01049   0.979    0.327
## 
## Residual standard error: 0.4782 on 11509 degrees of freedom
##   (3830 observations deleted due to missingness)
## Multiple R-squared(full model): 0.1173   Adjusted R-squared: 0.0603 
## Multiple R-squared(proj model): 8.333e-05   Adjusted R-squared: -0.06447 
## F-statistic(full model):2.058 on 743 and 11509 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model): 0.9591 on 1 and 11509 DF, p-value: 0.3274</code></pre>
<pre class="r"><code>directional.fe.c &lt;- felm(directional.updating ~ budget.treat |location.id|0|district_lower, data=data)
summary(directional.fe.c)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = directional.updating ~ budget.treat | location.id |      0 | district_lower, data = data) 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.8935 -0.4021 -0.2021  0.4769  0.9656 
## 
## Coefficients:
##              Estimate Cluster s.e. t value Pr(&gt;|t|)
## budget.treat  0.01027      0.01023   1.004    0.315
## 
## Residual standard error: 0.4782 on 11509 degrees of freedom
##   (3830 observations deleted due to missingness)
## Multiple R-squared(full model): 0.1173   Adjusted R-squared: 0.0603 
## Multiple R-squared(proj model): 8.333e-05   Adjusted R-squared: -0.06447 
## F-statistic(full model, *iid*):2.058 on 743 and 11509 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model): 1.009 on 1 and 26 DF, p-value: 0.3245</code></pre>
<pre class="r"><code>##Table harvesting
strict.coef &lt;- c(summary(strict)$coefficient[2,1],summary(strict.fe)$coefficient[1,1],summary(strict.fe.c)$coefficient[1,1])
strict.se &lt;- c(summary(strict)$coefficient[2,2],summary(strict.fe)$coefficient[1,2],summary(strict.fe.c)$coefficient[1,2])
strict.p &lt;- c(summary(strict)$coefficient[2,4],summary(strict.fe)$coefficient[1,4],summary(strict.fe.c)$coefficient[1,4])
strict.p.one.side &lt;- strict.p/2

updating.coef &lt;- c(summary(updating)$coefficient[2,1],summary(updating.fe)$coefficient[1,1],summary(updating.fe.c)$coefficient[1,1])
updating.se &lt;- c(summary(updating)$coefficient[2,2],summary(updating.fe)$coefficient[1,2],summary(updating.fe.c)$coefficient[1,2])
updating.p &lt;- c(summary(updating)$coefficient[2,4],summary(updating.fe)$coefficient[1,4],summary(updating.fe.c)$coefficient[1,4])
updating.p.one.side &lt;- updating.p/2

loose.coef &lt;- c(summary(loose)$coefficient[2,1],summary(loose.fe)$coefficient[1,1],summary(loose.fe.c)$coefficient[1,1])
loose.se &lt;- c(summary(loose)$coefficient[2,2],summary(loose.fe)$coefficient[1,2],summary(loose.fe.c)$coefficient[1,2])
loose.p &lt;- c(summary(loose)$coefficient[2,4],summary(loose.fe)$coefficient[1,4],summary(loose.fe.c)$coefficient[1,4])
loose.p.one.side &lt;- loose.p/2

loose.e.coef &lt;- c(summary(loose.e)$coefficient[2,1],summary(loose.e.fe)$coefficient[1,1],summary(loose.e.fe.c)$coefficient[1,1])
loose.e.se &lt;- c(summary(loose.e)$coefficient[2,2],summary(loose.e.fe)$coefficient[1,2],summary(loose.e.fe.c)$coefficient[1,2])
loose.e.p &lt;- c(summary(loose.e)$coefficient[2,4],summary(loose.e.fe)$coefficient[1,4],summary(loose.e.fe.c)$coefficient[1,4])
loose.e.p.one.side &lt;- loose.e.p/2

directional.coef &lt;- c(summary(directional)$coefficient[2,1],summary(directional.fe)$coefficient[1,1],summary(directional.fe.c)$coefficient[1,1])
directional.se &lt;- c(summary(directional)$coefficient[2,2],summary(directional.fe)$coefficient[1,2],summary(directional.fe.c)$coefficient[1,2])
directional.p &lt;- c(summary(directional)$coefficient[2,4],summary(directional.fe)$coefficient[1,4],summary(directional.fe.c)$coefficient[1,4])
directional.p.one.side &lt;- directional.p/2

#Formatting functions
coef.print &lt;- function(vector){
  sprintf(&quot;%.3f&quot;,round(vector,3))
}

se.print &lt;- function(vector){
  paste(&quot;(&quot;,sprintf(&quot;%.3f&quot;,round(vector,3)),&quot;)&quot;,sep=&quot;&quot;)
}

p.print &lt;- function(vector){
  paste(&quot;p=&quot;,sprintf(&quot;%.3f&quot;, round(vector,3)),sep=&quot;&quot;)
}

tab.dta &lt;- rbind(coef.print(strict.coef),se.print(strict.se),p.print(strict.p.one.side),
                 coef.print(updating.coef),se.print(updating.se),p.print(updating.p.one.side),
                 coef.print(loose.coef),se.print(loose.se),p.print(loose.p.one.side),
                 coef.print(loose.e.coef),se.print(loose.e.se),p.print(loose.e.p.one.side),
                 coef.print(directional.coef),se.print(directional.se),p.print(directional.p.one.side))
stargazer(tab.dta) #Mon, Mar 19, 2018 - 08:51:44</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Fri, Apr 27, 2018 - 20:53:35
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}} ccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
## 0.007 &amp; 0.004 &amp; 0.004 \\ 
## (0.007) &amp; (0.008) &amp; (0.008) \\ 
## p=0.180 &amp; p=0.328 &amp; p=0.325 \\ 
## 0.011 &amp; 0.009 &amp; 0.009 \\ 
## (0.009) &amp; (0.010) &amp; (0.010) \\ 
## p=0.104 &amp; p=0.183 &amp; p=0.161 \\ 
## 0.015 &amp; 0.020 &amp; 0.020 \\ 
## (0.009) &amp; (0.010) &amp; (0.008) \\ 
## p=0.041 &amp; p=0.024 &amp; p=0.006 \\ 
## 0.016 &amp; 0.021 &amp; 0.021 \\ 
## (0.009) &amp; (0.011) &amp; (0.008) \\ 
## p=0.042 &amp; p=0.025 &amp; p=0.004 \\ 
## 0.012 &amp; 0.010 &amp; 0.010 \\ 
## (0.009) &amp; (0.010) &amp; (0.010) \\ 
## p=0.086 &amp; p=0.164 &amp; p=0.158 \\ 
## \hline \\[-1.8ex] 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>#Note: some post-formatting in LaTex necessary


######################################################################################
###Table S24: Multiple, joint, and pooled hypothesis tests using randomization inference
######################################################################################

#Run the &quot;Figure 1&quot; block in BJNP_PNAS_replication.R

##Creating pooled datasets
budget.good.lc5.chair.comp$inc.vote &lt;- budget.good.lc5.chair.comp$lc5.chair.inc.vote
budget.good.lc5.chair.comp$intent &lt;- budget.good.lc5.chair.comp$lc5.chair.intent
budget.good.lc5.councillor.comp$inc.vote &lt;- budget.good.lc5.councillor.comp$lc5.councillor.inc.vote
budget.good.lc5.councillor.comp$intent &lt;- budget.good.lc5.councillor.comp$lc5.councillor.intent

budget.good.lc5.chair.comp$id.cleaned2 &lt;- budget.good.lc5.chair.comp$id.cleaned
budget.good.lc5.councillor.comp$id.cleaned2 &lt;- budget.good.lc5.councillor.comp$id.cleaned + 100000
budget.good.both &lt;- rbind(budget.good.lc5.chair.comp, budget.good.lc5.councillor.comp)

budget.bad.lc5.chair.comp$inc.vote &lt;- budget.bad.lc5.chair.comp$lc5.chair.inc.vote
budget.bad.lc5.chair.comp$intent &lt;- budget.bad.lc5.chair.comp$lc5.chair.intent
budget.bad.lc5.councillor.comp$inc.vote &lt;- budget.bad.lc5.councillor.comp$lc5.councillor.inc.vote
budget.bad.lc5.councillor.comp$intent &lt;- budget.bad.lc5.councillor.comp$lc5.councillor.intent

budget.bad.lc5.chair.comp$id.cleaned2 &lt;- budget.bad.lc5.chair.comp$id.cleaned
budget.bad.lc5.councillor.comp$id.cleaned2 &lt;- budget.bad.lc5.councillor.comp$id.cleaned + 100000
budget.bad.both &lt;- rbind(budget.bad.lc5.chair.comp, budget.bad.lc5.councillor.comp)

Budget_RI$id.cleaned2 &lt;- Budget_RI$id.cleaned
Budget_RI2 &lt;- Budget_RI
Budget_RI2$id.cleaned2 &lt;- Budget_RI2$id.cleaned + 100000
Budget_RI_both &lt;- rbind(Budget_RI,Budget_RI2)

##Analyzing pooled outcomes
both.good &lt;- felm(inc.vote ~ budget.treat + intent | location.id | 0 | id.cleaned,
                  data=budget.good.both)
summary(both.good)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = inc.vote ~ budget.treat + intent | location.id |      0 | id.cleaned, data = budget.good.both) 
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.01870 -0.36812  0.09373  0.30549  0.96028 
## 
## Coefficients:
##              Estimate Cluster s.e. t value Pr(&gt;|t|)    
## budget.treat  0.01274      0.01363   0.935     0.35    
## intent        0.08429      0.01220   6.906 5.45e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4312 on 6547 degrees of freedom
##   (6333 observations deleted due to missingness)
## Multiple R-squared(full model): 0.2462   Adjusted R-squared: 0.1948 
## Multiple R-squared(proj model): 0.008578   Adjusted R-squared: -0.05911 
## F-statistic(full model, *iid*):4.785 on 447 and 6547 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model): 24.73 on 2 and 4358 DF, p-value: 2.09e-11</code></pre>
<pre class="r"><code>both.bad &lt;- felm(inc.vote ~ budget.treat + intent | location.id | 0 | id.cleaned,
                 data=budget.bad.both)
summary(both.bad)</code></pre>
<pre><code>## 
## Call:
##    felm(formula = inc.vote ~ budget.treat + intent | location.id |      0 | id.cleaned, data = budget.bad.both) 
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.98697 -0.24405  0.05403  0.23317  0.87449 
## 
## Coefficients:
##              Estimate Cluster s.e. t value Pr(&gt;|t|)  
## budget.treat -0.01088      0.01235  -0.881   0.3785  
## intent        0.02381      0.01246   1.911   0.0561 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3931 on 5622 degrees of freedom
##   (4654 observations deleted due to missingness)
## Multiple R-squared(full model): 0.2957   Adjusted R-squared: 0.2467 
## Multiple R-squared(proj model): 0.0007932   Adjusted R-squared: -0.0687 
## F-statistic(full model, *iid*):6.036 on 391 and 5622 DF, p-value: &lt; 2.2e-16 
## F-statistic(proj model): 2.168 on 2 and 3796 DF, p-value: 0.1145</code></pre>
<pre class="r"><code>##Budget / Pooled results
both.good &lt;- felm.ri(inc.vote ~ budget.treat + intent | location.id | 0 | id.cleaned,
                     dta=budget.good.both, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI_both, 
                     rand.ob.info.cols = 4, join.var = &quot;id.cleaned2&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>both.bad &lt;- felm.ri(inc.vote ~ budget.treat + intent | location.id | 0 | id.cleaned, 
                    dta=budget.bad.both, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI_both, 
                    rand.ob.info.cols = 4, join.var = &quot;id.cleaned2&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>both.good.cov &lt;- felm.ri(inc.vote ~ budget.treat + intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | id.cleaned,
                         dta=budget.good.both, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI_both, 
                         rand.ob.info.cols = 4, join.var = &quot;id.cleaned2&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>both.bad.cov &lt;- felm.ri(inc.vote ~ budget.treat + intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | id.cleaned, 
                        dta=budget.bad.both, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI_both, 
                        rand.ob.info.cols = 4, join.var = &quot;id.cleaned2&quot;, sims=9999)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>## Joint test of significance
sims &lt;- 9999
ate.test.upper &lt;- both.good$ate
ate.test.lower &lt;- both.bad$ate

both.ate.upper.test &lt;- both.good$ate.samp.dist &gt; ate.test.upper
both.ate.lower.test &lt;- both.bad$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- both.ate.upper.test &amp; both.ate.lower.test
p.both.joint &lt;- sum(joint.test) / sims
p.both.joint #0.034</code></pre>
<pre><code>## [1] 0.03410341</code></pre>
<pre class="r"><code>ate.test.upper &lt;- both.good.cov$ate
ate.test.lower &lt;- both.bad.cov$ate

both.ate.upper.test &lt;- both.good.cov$ate.samp.dist &gt; ate.test.upper
both.ate.lower.test &lt;- both.bad.cov$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- both.ate.upper.test &amp; both.ate.lower.test
p.both.joint.cov &lt;- sum(joint.test) / sims
p.both.joint.cov #0.030</code></pre>
<pre><code>## [1] 0.03030303</code></pre>
<pre class="r"><code>#### Multiple hypothesis testing

sims &lt;- 9999

### All subjects, no covariates

## Correction w/in good news
#test: probability that at least one coefficient will be &gt;= largest realized ATE

ate.realized &lt;- c(chair.good$ate,coun.good$ate)
ate.test &lt;- max(ate.realized)

ate.chair.test &lt;- chair.good$ate.samp.dist &gt; ate.test
ate.coun.test &lt;- coun.good$ate.samp.dist &gt; ate.test

joint.test &lt;- ate.chair.test | ate.coun.test
p.good.joint &lt;- sum(joint.test) / sims
p.good.joint #0.111</code></pre>
<pre><code>## [1] 0.1105111</code></pre>
<pre class="r"><code>## Correction w/in bad news
#test: probability that at least one coefficient will be &gt;= largest realized ATE

ate.realized &lt;- c(chair.bad$ate,coun.bad$ate)
ate.test &lt;- min(ate.realized)

ate.chair.test &lt;- chair.bad$ate.samp.dist &lt; ate.test
ate.coun.test &lt;- coun.bad$ate.samp.dist &lt; ate.test

joint.test &lt;- ate.chair.test | ate.coun.test
p.bad.joint &lt;- sum(joint.test) / sims
p.bad.joint #0.030</code></pre>
<pre><code>## [1] 0.03010301</code></pre>
<pre class="r"><code>## Joint test w/ chairs
#test: probability that both coefficients will show strong effect in predicted direction under the sharp null than realized

ate.test.upper &lt;- chair.good$ate
ate.test.lower &lt;- chair.bad$ate

ate.good.test &lt;- chair.good$ate.samp.dist &gt; ate.test.upper
ate.bad.test &lt;- chair.bad$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- ate.good.test &amp; ate.bad.test
p.chair.joint &lt;- sum(joint.test) / sims
p.chair.joint #0.343</code></pre>
<pre><code>## [1] 0.3433343</code></pre>
<pre class="r"><code>## Joint test w/ councillors
#test: probability that both coefficients will show strong effect in predicted direction under the sharp null than realized

ate.test.upper &lt;- coun.good$ate
ate.test.lower &lt;- coun.bad$ate

ate.good.test &lt;- coun.good$ate.samp.dist &gt; ate.test.upper
ate.bad.test &lt;- coun.bad$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- ate.good.test &amp; ate.bad.test
p.coun.joint &lt;- sum(joint.test) / sims
p.coun.joint #0.001</code></pre>
<pre><code>## [1] 0.00070007</code></pre>
<pre class="r"><code>## Joint test of all offices
#test: probability that at least one good news effect will exceed max good news effect &amp; at least one bad news effect will exceed min bad news effect under the sharp null

ate.test.upper &lt;- max(chair.good$ate,coun.good$ate)
ate.test.lower &lt;- min(chair.bad$ate,coun.bad$ate)

chair.ate.good.test &lt;- chair.good$ate.samp.dist &gt; ate.test.upper
chair.ate.bad.test &lt;- chair.bad$ate.samp.dist &lt; ate.test.lower
coun.ate.good.test &lt;- coun.good$ate.samp.dist &gt; ate.test.upper
coun.ate.bad.test &lt;- coun.bad$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- (chair.ate.good.test | coun.ate.good.test) &amp; (chair.ate.bad.test | coun.ate.bad.test)
p.joint &lt;- sum(joint.test) / sims
p.joint #0.003</code></pre>
<pre><code>## [1] 0.00310031</code></pre>
<pre class="r"><code>### All subjects, covariates

## Correction w/in good news
#test: probability that at least one coefficient will be &gt;= largest realized ATE

ate.realized &lt;- c(chair.good.cov$ate,coun.good.cov$ate)
ate.test &lt;- max(ate.realized)

ate.chair.test &lt;- chair.good.cov$ate.samp.dist &gt; ate.test
ate.coun.test &lt;- coun.good.cov$ate.samp.dist &gt; ate.test

joint.test &lt;- ate.chair.test | ate.coun.test
p.good.cov.joint &lt;- sum(joint.test) / sims
p.good.cov.joint #0.103</code></pre>
<pre><code>## [1] 0.1031103</code></pre>
<pre class="r"><code>## Correction w/in bad news
#test: probability that at least one coefficient will be &gt;= largest realized ATE

ate.realized &lt;- c(chair.bad.cov$ate,coun.bad.cov$ate)
ate.test &lt;- min(ate.realized)

ate.chair.test &lt;- chair.bad.cov$ate.samp.dist &lt; ate.test
ate.coun.test &lt;- coun.bad.cov$ate.samp.dist &lt; ate.test

joint.test &lt;- ate.chair.test | ate.coun.test
p.bad.cov.joint &lt;- sum(joint.test) / sims
p.bad.cov.joint #0.025</code></pre>
<pre><code>## [1] 0.02480248</code></pre>
<pre class="r"><code>## Joint test w/ chairs
#test: probability that both coefficients will show strong effect in predicted direction under the sharp null than realized

ate.test.upper &lt;- chair.good.cov$ate
ate.test.lower &lt;- chair.bad.cov$ate

ate.good.cov.test &lt;- chair.good.cov$ate.samp.dist &gt; ate.test.upper
ate.bad.cov.test &lt;- chair.bad.cov$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- ate.good.cov.test &amp; ate.bad.cov.test
p.chair.cov.joint &lt;- sum(joint.test) / sims
p.chair.cov.joint #0.327</code></pre>
<pre><code>## [1] 0.3269327</code></pre>
<pre class="r"><code>## Joint test w/ councillors
#test: probability that both coefficients will show strong effect in predicted direction under the sharp null than realized

ate.test.upper &lt;- coun.good.cov$ate
ate.test.lower &lt;- coun.bad.cov$ate

ate.good.cov.test &lt;- coun.good.cov$ate.samp.dist &gt; ate.test.upper
ate.bad.cov.test &lt;- coun.bad.cov$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- ate.good.cov.test &amp; ate.bad.cov.test
p.coun.cov.joint &lt;- sum(joint.test) / sims
p.coun.cov.joint #0.001</code></pre>
<pre><code>## [1] 0.00060006</code></pre>
<pre class="r"><code>## Joint test of all offices
#test: probability that at least one good news effect will exceed max good news effect &amp; at least one bad news effect will exceed min bad news effect under the sharp null

ate.test.upper &lt;- max(chair.good.cov$ate,coun.good.cov$ate)
ate.test.lower &lt;- min(chair.bad.cov$ate,coun.bad.cov$ate)

chair.ate.good.cov.test &lt;- chair.good.cov$ate.samp.dist &gt; ate.test.upper
chair.ate.bad.cov.test &lt;- chair.bad.cov$ate.samp.dist &lt; ate.test.lower
coun.ate.good.cov.test &lt;- coun.good.cov$ate.samp.dist &gt; ate.test.upper
coun.ate.bad.cov.test &lt;- coun.bad.cov$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- (chair.ate.good.cov.test | coun.ate.good.cov.test) &amp; (chair.ate.bad.cov.test | coun.ate.bad.cov.test)
p.joint.cov &lt;- sum(joint.test) / sims
p.joint.cov #0.002</code></pre>
<pre><code>## [1] 0.00240024</code></pre>
<pre class="r"><code>### Verified recipients, no covariates

## Correction w/in good.c news
#test: probability that at least one coefficient will be &gt;= largest realized ATE

ate.realized &lt;- c(chair.good.c$ate,coun.good.c$ate)
ate.test &lt;- max(ate.realized)

ate.chair.test &lt;- chair.good.c$ate.samp.dist &gt; ate.test
ate.coun.test &lt;- coun.good.c$ate.samp.dist &gt; ate.test

joint.test &lt;- ate.chair.test | ate.coun.test
p.good.c.joint &lt;- sum(joint.test) / sims
p.good.c.joint #0.033</code></pre>
<pre><code>## [1] 0.03290329</code></pre>
<pre class="r"><code>## Correction w/in bad.c news
#test: probability that at least one coefficient will be &gt;= largest realized ATE

ate.realized &lt;- c(chair.bad.c$ate,coun.bad.c$ate)
ate.test &lt;- min(ate.realized)

ate.chair.test &lt;- chair.bad.c$ate.samp.dist &lt; ate.test
ate.coun.test &lt;- coun.bad.c$ate.samp.dist &lt; ate.test

joint.test &lt;- ate.chair.test | ate.coun.test
p.bad.c.joint &lt;- sum(joint.test) / sims
p.bad.c.joint #0.004</code></pre>
<pre><code>## [1] 0.00370037</code></pre>
<pre class="r"><code>## Joint test w/ chairs
#test: probability that both coefficients will show strong effect in predicted direction under the sharp null than realized

ate.test.upper &lt;- chair.good.c$ate
ate.test.lower &lt;- chair.bad.c$ate

ate.good.c.test &lt;- chair.good.c$ate.samp.dist &gt; ate.test.upper
ate.bad.c.test &lt;- chair.bad.c$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- ate.good.c.test &amp; ate.bad.c.test
p.chair.c.joint &lt;- sum(joint.test) / sims
p.chair.c.joint #0.148</code></pre>
<pre><code>## [1] 0.1476148</code></pre>
<pre class="r"><code>## Joint test w/ councillors
#test: probability that both coefficients will show strong effect in predicted direction under the sharp null than realized

ate.test.upper &lt;- coun.good.c$ate
ate.test.lower &lt;- coun.bad.c$ate

ate.good.c.test &lt;- coun.good.c$ate.samp.dist &gt; ate.test.upper
ate.bad.c.test &lt;- coun.bad.c$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- ate.good.c.test &amp; ate.bad.c.test
p.coun.c.joint &lt;- sum(joint.test) / sims
p.coun.c.joint #0.000</code></pre>
<pre><code>## [1] 0</code></pre>
<pre class="r"><code>## Joint test of all offices
#test: probability that at least one good.c news effect will exceed max good.c news effect &amp; at least one bad.c news effect will exceed min bad.c news effect under the sharp null

ate.test.upper &lt;- max(chair.good.c$ate,coun.good.c$ate)
ate.test.lower &lt;- min(chair.bad.c$ate,coun.bad.c$ate)

chair.ate.good.c.test &lt;- chair.good.c$ate.samp.dist &gt; ate.test.upper
chair.ate.bad.c.test &lt;- chair.bad.c$ate.samp.dist &lt; ate.test.lower
coun.ate.good.c.test &lt;- coun.good.c$ate.samp.dist &gt; ate.test.upper
coun.ate.bad.c.test &lt;- coun.bad.c$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- (chair.ate.good.c.test | coun.ate.good.c.test) &amp; (chair.ate.bad.c.test | coun.ate.bad.c.test)
p.joint.c &lt;- sum(joint.test) / sims
p.joint.c #0.000</code></pre>
<pre><code>## [1] 0</code></pre>
<pre class="r"><code>### Verified recipients, covariates

## Correction w/in good news
#test: probability that at least one coefficient will be &gt;= largest realized ATE

ate.realized &lt;- c(chair.good.cov.c$ate,coun.good.cov.c$ate)
ate.test &lt;- max(ate.realized)

ate.chair.test &lt;- chair.good.cov.c$ate.samp.dist &gt; ate.test
ate.coun.test &lt;- coun.good.cov.c$ate.samp.dist &gt; ate.test

joint.test &lt;- ate.chair.test | ate.coun.test
p.good.cov.c.joint &lt;- sum(joint.test) / sims
p.good.cov.c.joint #0.027</code></pre>
<pre><code>## [1] 0.02680268</code></pre>
<pre class="r"><code>## Correction w/in bad news
#test: probability that at least one coefficient will be &gt;= largest realized ATE

ate.realized &lt;- c(chair.bad.cov.c$ate,coun.bad.cov.c$ate)
ate.test &lt;- min(ate.realized)

ate.chair.test &lt;- chair.bad.cov.c$ate.samp.dist &lt; ate.test
ate.coun.test &lt;- coun.bad.cov.c$ate.samp.dist &lt; ate.test

joint.test &lt;- ate.chair.test | ate.coun.test
p.bad.cov.c.joint &lt;- sum(joint.test) / sims
p.bad.cov.c.joint #0.004</code></pre>
<pre><code>## [1] 0.00350035</code></pre>
<pre class="r"><code>## Joint test w/ chairs
#test: probability that both coefficients will show strong effect in predicted direction under the sharp null than realized

ate.test.upper &lt;- chair.good.cov.c$ate
ate.test.lower &lt;- chair.bad.cov.c$ate

ate.good.cov.c.test &lt;- chair.good.cov.c$ate.samp.dist &gt; ate.test.upper
ate.bad.cov.c.test &lt;- chair.bad.cov.c$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- ate.good.cov.c.test &amp; ate.bad.cov.c.test
p.chair.cov.c.joint &lt;- sum(joint.test) / sims
p.chair.cov.c.joint #0.121</code></pre>
<pre><code>## [1] 0.1210121</code></pre>
<pre class="r"><code>## Joint test w/ councillors
#test: probability that both coefficients will show strong effect in predicted direction under the sharp null than realized

ate.test.upper &lt;- coun.good.cov.c$ate
ate.test.lower &lt;- coun.bad.cov.c$ate

ate.good.cov.c.test &lt;- coun.good.cov.c$ate.samp.dist &gt; ate.test.upper
ate.bad.cov.c.test &lt;- coun.bad.cov.c$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- ate.good.cov.c.test &amp; ate.bad.cov.c.test
p.coun.cov.c.joint &lt;- sum(joint.test) / sims
p.coun.cov.c.joint #0.000</code></pre>
<pre><code>## [1] 0</code></pre>
<pre class="r"><code>## Joint test of all offices
#test: probability that at least one good news effect will exceed max good news effect &amp; at least one bad news effect will exceed min bad news effect under the sharp null

ate.test.upper &lt;- max(chair.good.cov.c$ate,coun.good.cov.c$ate)
ate.test.lower &lt;- min(chair.bad.cov.c$ate,coun.bad.cov.c$ate)

chair.ate.good.cov.c.test &lt;- chair.good.cov.c$ate.samp.dist &gt; ate.test.upper
chair.ate.bad.cov.c.test &lt;- chair.bad.cov.c$ate.samp.dist &lt; ate.test.lower
coun.ate.good.cov.c.test &lt;- coun.good.cov.c$ate.samp.dist &gt; ate.test.upper
coun.ate.bad.cov.c.test &lt;- coun.bad.cov.c$ate.samp.dist &lt; ate.test.lower

joint.test &lt;- (chair.ate.good.cov.c.test | coun.ate.good.cov.c.test) &amp; (chair.ate.bad.cov.c.test | coun.ate.bad.cov.c.test)
p.joint.cov.c &lt;- sum(joint.test) / sims
p.joint.cov.c #0.000</code></pre>
<pre><code>## [1] 0</code></pre>
<pre class="r"><code>test &lt;- c(&quot;$\\text{max}(\\tilde{b}_{+}) &gt; \\text{max}(b_{+}^*)$&quot;,
          &quot;$\\text{min}(\\tilde{b}_{-}) &lt; \\text{min}(b_{-}^*)$&quot;,
          &quot;$\\tilde{b}_{\\text{chair,+}} &gt; \\text{max}(b_{\\text{chair}}^*) \\cap \\tilde{b}_{\\text{chair,-}} &lt; \\text{min}(b_{\\text{chair}}^*)$&quot;,
          &quot;$\\tilde{b}_{\\text{councillor,+}} &gt; \\text{max}(b_{\\text{councillor}}^*) \\cap \\tilde{b}_{\\text{councillor,-}} &lt; \\text{min}(b_{\\text{councillor}}^*)$&quot;,
          &quot;$\\text{max}(\\tilde{b}_{+}) &gt; \\text{max}(b_{\\text{+}}^*) \\cap \\text{min}(\\tilde{b}_{\\text{-}}) &lt; \\text{min}(b_{\\text{-}}^*)$&quot;,
          &quot;$\\tilde{b}_{\\text{pooled,+}} &gt; b_{\\text{pooled,+}}^*$&quot;,
          &quot;$\\tilde{b}_{\\text{pooled,-}} &lt; b_{\\text{pooled,-}}^*$&quot;,
          &quot;$\\tilde{b}_{\\text{pooled,+}} &gt; b_{\\text{pooled,+}}^* \\cap \\tilde{b}_{\\text{pooled,-}} &lt; b_{\\text{pooled,-}}^*$&quot;)

p &lt;- c(p.good.joint,
       p.bad.joint,
       p.chair.joint,
       p.coun.joint,
       p.joint,
       both.good$p.one.way.greater,
       both.bad$p.one.way.lesser,
       p.both.joint)
p.cov &lt;- c(p.good.cov.joint,
           p.bad.cov.joint,
           p.chair.cov.joint,
           p.coun.cov.joint,
           p.joint.cov,
           both.good.cov$p.one.way.greater,
           both.bad.cov$p.one.way.lesser,
           p.both.joint.cov)
p.c &lt;- c(p.good.c.joint,
         p.bad.c.joint,
         p.chair.c.joint,
         p.coun.c.joint,
         p.joint.c,
         NA,
         NA,
         NA)
p.cov.c &lt;- c(p.good.cov.c.joint,
             p.bad.cov.c.joint,
             p.chair.cov.c.joint,
             p.coun.cov.c.joint,
             p.joint.cov.c,
             NA,
             NA,
             NA)

tab.dta &lt;- data.frame(test,p,p.cov,p.c,p.cov.c)
print(xtable(tab.dta, digits=4), include.rownames=FALSE, sanitize.text.function=identity)</code></pre>
<pre><code>## % latex table generated in R 3.5.0 by xtable 1.8-2 package
## % Fri Apr 27 22:17:39 2018
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrr}
##   \hline
## test &amp; p &amp; p.cov &amp; p.c &amp; p.cov.c \\ 
##   \hline
## $\text{max}(\tilde{b}_{+}) &gt; \text{max}(b_{+}^*)$ &amp; 0.1105 &amp; 0.1031 &amp; 0.0329 &amp; 0.0268 \\ 
##   $\text{min}(\tilde{b}_{-}) &lt; \text{min}(b_{-}^*)$ &amp; 0.0301 &amp; 0.0248 &amp; 0.0037 &amp; 0.0035 \\ 
##   $\tilde{b}_{\text{chair,+}} &gt; \text{max}(b_{\text{chair}}^*) \cap \tilde{b}_{\text{chair,-}} &lt; \text{min}(b_{\text{chair}}^*)$ &amp; 0.3433 &amp; 0.3269 &amp; 0.1476 &amp; 0.1210 \\ 
##   $\tilde{b}_{\text{councillor,+}} &gt; \text{max}(b_{\text{councillor}}^*) \cap \tilde{b}_{\text{councillor,-}} &lt; \text{min}(b_{\text{councillor}}^*)$ &amp; 0.0007 &amp; 0.0006 &amp; 0.0000 &amp; 0.0000 \\ 
##   $\text{max}(\tilde{b}_{+}) &gt; \text{max}(b_{\text{+}}^*) \cap \text{min}(\tilde{b}_{\text{-}}) &lt; \text{min}(b_{\text{-}}^*)$ &amp; 0.0031 &amp; 0.0024 &amp; 0.0000 &amp; 0.0000 \\ 
##   $\tilde{b}_{\text{pooled,+}} &gt; b_{\text{pooled,+}}^*$ &amp; 0.1737 &amp; 0.1742 &amp;  &amp;  \\ 
##   $\tilde{b}_{\text{pooled,-}} &lt; b_{\text{pooled,-}}^*$ &amp; 0.1848 &amp; 0.1575 &amp;  &amp;  \\ 
##   $\tilde{b}_{\text{pooled,+}} &gt; b_{\text{pooled,+}}^* \cap \tilde{b}_{\text{pooled,-}} &lt; b_{\text{pooled,-}}^*$ &amp; 0.0341 &amp; 0.0303 &amp;  &amp;  \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>######################################################################################
###Table S25: Main results (Figure 1) with fixed-effects weights applied
######################################################################################

##### Main Subset Effects #####

chair.good &lt;- felm.ri3(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name,
                      dta=budget.good.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                      rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                      weights=budget.good.lc5.chair.comp$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.bad &lt;- felm.ri3(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, 
                     dta=budget.bad.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                     rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                     weights=budget.bad.lc5.chair.comp$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.good.cov &lt;- felm.ri3(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name,
                          dta=budget.good.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                          rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                          weights=budget.good.lc5.chair.comp$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.bad.cov &lt;- felm.ri3(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, 
                         dta=budget.bad.lc5.chair.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                         rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                         weights=budget.bad.lc5.chair.comp$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.good &lt;- felm.ri3(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name,
                     dta=budget.good.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                     rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                     weights=budget.good.lc5.councillor.comp$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.bad &lt;- felm.ri3(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, 
                    dta=budget.bad.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                    rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                    weights=budget.bad.lc5.councillor.comp$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.good.cov &lt;- felm.ri3(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name,
                         dta=budget.good.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                         rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                         weights=budget.good.lc5.councillor.comp$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.bad.cov &lt;- felm.ri3(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, 
                        dta=budget.bad.lc5.councillor.comp, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                        rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                        weights=budget.bad.lc5.councillor.comp$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>##### Verified Recipient Effects #####

chair.good.c &lt;- felm.ri3(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name,
                        dta=budget.good.lc5.chair.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                        rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                        weights = budget.good.lc5.chair.comp.c$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.bad.c &lt;- felm.ri3(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent | location.id | 0 | lc5.chair.name, 
                       dta=budget.bad.lc5.chair.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                       rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                       weights = budget.bad.lc5.chair.comp.c$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.good.cov.c &lt;- felm.ri3(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name,
                            dta=budget.good.lc5.chair.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                            rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                            weights = budget.good.lc5.chair.comp.c$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>chair.bad.cov.c &lt;- felm.ri3(lc5.chair.inc.vote ~ budget.treat + lc5.chair.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.chair.name, 
                           dta=budget.bad.lc5.chair.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                           rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                           weights = budget.bad.lc5.chair.comp.c$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.good.c &lt;- felm.ri3(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name,
                       dta=budget.good.lc5.councillor.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                       rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                       weights=budget.good.lc5.councillor.comp.c$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.bad.c &lt;- felm.ri3(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent | location.id | 0 | lc5.councillor.name, 
                      dta=budget.bad.lc5.councillor.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                      rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                      weights=budget.bad.lc5.councillor.comp.c$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.good.cov.c &lt;- felm.ri3(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name,
                           dta=budget.good.lc5.councillor.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                           rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                           weights=budget.good.lc5.councillor.comp.c$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>coun.bad.cov.c &lt;- felm.ri3(lc5.councillor.inc.vote ~ budget.treat + lc5.councillor.intent + b.Q1_living_conditions + b.Q21b_Trust_in_Twaweza_for_info + b.Q24_Powerful_ppl_learning_how_ + b.Q25_Will_counting_votes_be_fai + r.What_is_your_gender + r.education + r.What_is_your_age | location.id | 0 | lc5.councillor.name, 
                          dta=budget.bad.lc5.councillor.comp.c, treat.var = &quot;budget.treat&quot;, rand.ob=Budget_RI, 
                          rand.ob.info.cols = 4, join.var = &quot;id.cleaned&quot;, sims=9999,
                          weights=budget.bad.lc5.councillor.comp.c$fe.w)</code></pre>
<pre><code>## [1] &quot;1. data and rand.ob must have rows organized in identical order&quot;
## [1] &quot;2. treat.var should be first right-side entry in formula&quot;
## [1] &quot;3. join.var must have identical name between data and rand.ob&quot;</code></pre>
<pre class="r"><code>all.ate &lt;- c(chair.good$ate,
             chair.good.cov$ate,
             coun.good$ate,
             coun.good.cov$ate,
             chair.bad$ate,
             chair.bad.cov$ate,
             coun.bad$ate,
             coun.bad.cov$ate)
all.se &lt;- c(chair.good$se,
             chair.good.cov$se,
             coun.good$se,
             coun.good.cov$se,
             chair.bad$se,
             chair.bad.cov$se,
             coun.bad$se,
             coun.bad.cov$se)
all.p &lt;- c(chair.good$p.one.way.greater,
            chair.good.cov$p.one.way.greater,
            coun.good$p.one.way.greater,
            coun.good.cov$p.one.way.greater,
            chair.bad$p.one.way.lesser,
            chair.bad.cov$p.one.way.lesser,
            coun.bad$p.one.way.lesser,
            coun.bad.cov$p.one.way.lesser)

ver.ate &lt;- c(chair.good.c$ate,
             chair.good.cov.c$ate,
             coun.good.c$ate,
             coun.good.cov.c$ate,
             chair.bad.c$ate,
             chair.bad.cov.c$ate,
             coun.bad.c$ate,
             coun.bad.cov.c$ate)
ver.se &lt;- c(chair.good.c$se,
            chair.good.cov.c$se,
            coun.good.c$se,
            coun.good.cov.c$se,
            chair.bad.c$se,
            chair.bad.cov.c$se,
            coun.bad.c$se,
            coun.bad.cov.c$se)
ver.p &lt;- c(chair.good.c$p.one.way.greater,
           chair.good.cov.c$p.one.way.greater,
           coun.good.c$p.one.way.greater,
           coun.good.cov.c$p.one.way.greater,
           chair.bad.c$p.one.way.lesser,
           chair.bad.cov.c$p.one.way.lesser,
           coun.bad.c$p.one.way.lesser,
           coun.bad.cov.c$p.one.way.lesser)

#Formatting functions
coef.print &lt;- function(vector){
  sprintf(&quot;%.3f&quot;,round(vector,3))
}

se.print &lt;- function(vector){
  paste(&quot;(&quot;,sprintf(&quot;%.3f&quot;,round(vector,3)),&quot;)&quot;,sep=&quot;&quot;)
}

p.print &lt;- function(vector){
  paste(&quot;p=&quot;,sprintf(&quot;%.3f&quot;, round(vector,3)),sep=&quot;&quot;)
}

all.dta &lt;- rbind(coef.print(all.ate),
                 se.print(all.se),
                 p.print(all.p))
stargazer(all.dta) #Post-formating in LaTex required</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Sat, Apr 28, 2018 - 02:18:45
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}} cccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
## 0.002 &amp; 0.001 &amp; 0.022 &amp; 0.023 &amp; 0.006 &amp; 0.005 &amp; -0.036 &amp; -0.037 \\ 
## (0.017) &amp; (0.017) &amp; (0.018) &amp; (0.018) &amp; (0.016) &amp; (0.016) &amp; (0.016) &amp; (0.016) \\ 
## p=0.460 &amp; p=0.467 &amp; p=0.107 &amp; p=0.101 &amp; p=0.648 &amp; p=0.615 &amp; p=0.011 &amp; p=0.010 \\ 
## \hline \\[-1.8ex] 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>ver.dta &lt;- rbind(coef.print(ver.ate),
                 se.print(ver.se),
                 p.print(ver.p))
stargazer(ver.dta) #Post-formating in LaTex required</code></pre>
<pre><code>## 
## % Table created by stargazer v.5.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Sat, Apr 28, 2018 - 02:18:45
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}} cccccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
## 0.005 &amp; 0.006 &amp; 0.044 &amp; 0.047 &amp; -0.012 &amp; -0.014 &amp; -0.056 &amp; -0.056 \\ 
## (0.022) &amp; (0.022) &amp; (0.023) &amp; (0.024) &amp; (0.019) &amp; (0.019) &amp; (0.020) &amp; (0.020) \\ 
## p=0.413 &amp; p=0.388 &amp; p=0.031 &amp; p=0.024 &amp; p=0.262 &amp; p=0.224 &amp; p=0.002 &amp; p=0.002 \\ 
## \hline \\[-1.8ex] 
## \end{tabular} 
## \end{table}</code></pre>
<pre class="r"><code>options(warn=0)
#Note: warnings turned back on after compiling replication report</code></pre>




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